Predictive Big Data Analytics (PBDA) for Parkinson’s Disease
SOCR Team
September 18, 2016
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install required packages
install.packages("geepack",repos="http://cran.us.r-project.org")## Installing package into '/Users/MingT/Library/R/3.3/library'
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## /var/folders/s8/0_mb4tr92mlfhspqc783k8z80000gn/T//RtmpwzaaGc/downloaded_packagesinstall.packages(c("lme4","mi","MASS"),repos="http://cran.us.r-project.org")## Installing packages into '/Users/MingT/Library/R/3.3/library'
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## /var/folders/s8/0_mb4tr92mlfhspqc783k8z80000gn/T//RtmpwzaaGc/downloaded_packagesinstall.packages("class",repos="http://cran.us.r-project.org")## Installing package into '/Users/MingT/Library/R/3.3/library'
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## /var/folders/s8/0_mb4tr92mlfhspqc783k8z80000gn/T//RtmpwzaaGc/downloaded_packagesinstall.packages("mi",repos="http://cran.us.r-project.org") ## Installing package into '/Users/MingT/Library/R/3.3/library'
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## /var/folders/s8/0_mb4tr92mlfhspqc783k8z80000gn/T//RtmpwzaaGc/downloaded_packagesoptions(warn=-1) ####data manipulation #######
setwd("~/documents/PPMI") #set working director
clinical<-read.csv("ppmi_clinical.csv",header=TRUE) #read raw data
colnames(clinical)[2]<-"FID_IID"
ppmi_new<-read.csv("PPMI_GSA_clinical.csv",header=TRUE)
merged_dataset<-merge(ppmi_new,clinical,by="FID_IID",all.x=TRUE) #merge clinical data with brain image
write.csv(merged_dataset,"ppmi_clinical_complete.csv")
ppmi<-merged_dataset[,c(1:399,820:823,833,832,816)] #Select "tier 1" UPDRSs
head(ppmi)## FID_IID X.x L_insular_cortex_AvgMeanCurvature
## 1 3000 1 0.10339303
## 2 3001 2 0.11044503
## 3 3002 3 0.10091019
## 4 3003 4 0.09726433
## 5 3004 5 0.10955031
## 6 3006 6 0.09733524
## L_insular_cortex_ComputeArea L_insular_cortex_Volume
## 1 2763.365 8779.951
## 2 2917.938 8754.107
## 3 2505.515 8089.185
## 4 3284.796 11446.049
## 5 2802.992 9000.509
## 6 3188.515 11004.131
## L_insular_cortex_ShapeIndex L_insular_cortex_Curvedness
## 1 0.3192754 0.1094209
## 2 0.3207401 0.1033839
## 3 0.3191578 0.1099705
## 4 0.3150762 0.0991298
## 5 0.3272781 0.1144485
## 6 0.3040458 0.1091740
## R_insular_cortex_AvgMeanCurvature R_insular_cortex_ComputeArea
## 1 0.1159638 2291.633
## 2 0.1160668 2342.961
## 3 0.1077885 2073.981
## 4 0.1014219 2320.993
## 5 0.1126108 2225.154
## 6 0.1086814 2135.745
## R_insular_cortex_Volume R_insular_cortex_ShapeIndex
## 1 5482.136 0.2664319
## 2 5732.367 0.3149570
## 3 5321.370 0.2709259
## 4 6395.721 0.3003472
## 5 5567.333 0.3058957
## 6 5583.177 0.3187932
## R_insular_cortex_Curvedness L_cingulate_gyrus_AvgMeanCurvature
## 1 0.1292846 0.1332509
## 2 0.1215921 0.1246343
## 3 0.1345651 0.1355602
## 4 0.1126387 0.1236226
## 5 0.1315195 0.1274116
## 6 0.1205962 0.1298253
## L_cingulate_gyrus_ComputeArea L_cingulate_gyrus_Volume
## 1 3477.519 8072.617
## 2 4381.930 11205.127
## 3 3221.540 7439.645
## 4 4658.949 12443.624
## 5 4016.731 9922.555
## 6 4188.552 10502.193
## L_cingulate_gyrus_ShapeIndex L_cingulate_gyrus_Curvedness
## 1 0.4406856 0.08024956
## 2 0.4349292 0.06714417
## 3 0.4396813 0.08652417
## 4 0.4556403 0.06749613
## 5 0.4316057 0.07150800
## 6 0.4510032 0.07495887
## R_cingulate_gyrus_AvgMeanCurvature R_cingulate_gyrus_ComputeArea
## 1 0.1319288 4131.343
## 2 0.1249111 4610.447
## 3 0.1458248 3194.348
## 4 0.1361298 4028.330
## 5 0.1288771 3918.263
## 6 0.1277486 4352.117
## R_cingulate_gyrus_Volume R_cingulate_gyrus_ShapeIndex
## 1 10526.958 0.4419433
## 2 12246.549 0.4350192
## 3 7264.683 0.4268223
## 4 9681.935 0.4498362
## 5 10227.022 0.4462917
## 6 11528.466 0.4498547
## R_cingulate_gyrus_Curvedness L_caudate_AvgMeanCurvature
## 1 0.08182803 0.2011498
## 2 0.07081687 0.2392929
## 3 0.09480283 0.1833535
## 4 0.08071711 0.1983211
## 5 0.07295120 0.1958644
## 6 0.07407571 0.2115839
## L_caudate_ComputeArea L_caudate_Volume L_caudate_ShapeIndex
## 1 801.8746 1207.2003 0.2811706
## 2 621.5344 821.8991 0.2473931
## 3 876.9414 1364.8601 0.2580652
## 4 1019.6664 1570.3055 0.3164872
## 5 1086.9388 1772.9241 0.2960296
## 6 863.4775 1341.3073 0.2780000
## L_caudate_Curvedness R_caudate_AvgMeanCurvature R_caudate_ComputeArea
## 1 0.2364463 0.1884890 999.3843
## 2 0.3429220 0.1558452 1302.1458
## 3 0.2308683 0.1763202 1056.2200
## 4 0.1760060 0.1703131 1208.7987
## 5 0.1854576 0.1739900 1267.7040
## 6 0.2513109 0.1441599 1247.1482
## R_caudate_Volume R_caudate_ShapeIndex R_caudate_Curvedness
## 1 1698.516 0.2611800 0.1978461
## 2 2526.248 0.2892424 0.1496714
## 3 1965.206 0.2970232 0.1854200
## 4 2268.701 0.2912487 0.1568764
## 5 2262.694 0.3027961 0.1599818
## 6 2678.534 0.3008528 0.1360011
## L_putamen_AvgMeanCurvature L_putamen_ComputeArea L_putamen_Volume
## 1 0.1623982 1261.750 2595.533
## 2 0.2022735 1029.175 1543.017
## 3 0.1418179 1275.905 2696.695
## 4 0.1409578 1482.684 3046.274
## 5 0.1814492 1050.227 2028.140
## 6 0.1787384 1229.513 2482.513
## L_putamen_ShapeIndex L_putamen_Curvedness R_putamen_AvgMeanCurvature
## 1 0.2857386 0.1766909 0.1209783
## 2 0.2853486 0.2320214 0.1111208
## 3 0.2876249 0.1566615 0.1446244
## 4 0.3041529 0.1523438 0.1402802
## 5 0.2769987 0.1868594 0.1214390
## 6 0.2923973 0.2064697 0.1628602
## R_putamen_ComputeArea R_putamen_Volume R_putamen_ShapeIndex
## 1 1471.391 3429.283 0.2861552
## 2 1680.197 3792.201 0.2670927
## 3 1375.725 2966.682 0.2936447
## 4 1310.875 2755.439 0.2913030
## 5 1511.374 3458.706 0.2978874
## 6 1298.760 2595.811 0.3136044
## R_putamen_Curvedness L_hippocampus_AvgMeanCurvature
## 1 0.1400700 0.1332656
## 2 0.1332011 0.1226265
## 3 0.1546469 0.1304512
## 4 0.1606766 0.1191435
## 5 0.1362302 0.1242598
## 6 0.1666391 0.1116450
## L_hippocampus_ComputeArea L_hippocampus_Volume L_hippocampus_ShapeIndex
## 1 1456.701 3441.663 0.3149792
## 2 1769.672 4737.038 0.3201427
## 3 1529.759 3736.040 0.3189372
## 4 1679.536 4458.264 0.3512617
## 5 1552.218 3718.433 0.2814200
## 6 1878.617 5078.684 0.3338252
## L_hippocampus_Curvedness R_hippocampus_AvgMeanCurvature
## 1 0.1491479 0.1150244
## 2 0.1179703 0.1312485
## 3 0.1239492 0.1179286
## 4 0.1092478 0.1174198
## 5 0.1435228 0.1158403
## 6 0.1089977 0.1140703
## R_hippocampus_ComputeArea R_hippocampus_Volume R_hippocampus_ShapeIndex
## 1 1825.942 4843.637 0.3148975
## 2 1578.946 3817.621 0.2894075
## 3 1799.438 4665.167 0.3102413
## 4 1850.187 5177.618 0.3050725
## 5 1762.969 4529.123 0.3331539
## 6 1976.793 5508.145 0.3469782
## R_hippocampus_Curvedness cerebellum_AvgMeanCurvature
## 1 0.11740466 0.11124426
## 2 0.14824928 -0.01081359
## 3 0.12306355 2.66084380
## 4 0.12038557 0.03830412
## 5 0.12558195 0.07386941
## 6 0.09903943 -0.15067070
## cerebellum_ComputeArea cerebellum_Volume cerebellum_ShapeIndex
## 1 17233.65 148735.2 0.3834653
## 2 20909.58 185742.6 0.2932517
## 3 17627.01 155632.3 0.3074467
## 4 15506.38 133878.8 0.3423785
## 5 17130.48 141371.6 0.4104847
## 6 17778.92 155220.7 0.3737010
## cerebellum_Curvedness brainstem_AvgMeanCurvature brainstem_ComputeArea
## 1 0.06011980 0.06972784 6394.451
## 2 0.16746150 0.06346277 7741.742
## 3 34.49791700 0.07059487 6456.809
## 4 0.11113414 0.06736263 6987.226
## 5 0.04983775 0.06649145 6979.882
## 6 2.31151870 0.07263206 7253.218
## brainstem_Volume brainstem_ShapeIndex brainstem_Curvedness
## 1 35754.04 0.3087066 0.07605392
## 2 45610.19 0.3192699 0.07574226
## 3 35781.50 0.2843547 0.08571674
## 4 41214.97 0.3308024 0.07034704
## 5 40971.91 0.3150985 0.07551081
## 6 43166.55 0.3444916 0.06780533
## L_superior_frontal_gyrus_AvgMeanCurvature
## 1 0.08100099
## 2 0.07951369
## 3 0.07711980
## 4 0.07734055
## 5 0.07925338
## 6 0.07307774
## L_superior_frontal_gyrus_ComputeArea L_superior_frontal_gyrus_Volume
## 1 10323.433 48405.02
## 2 11363.699 53308.55
## 3 9510.621 43467.55
## 4 11380.541 53081.02
## 5 10844.077 54149.27
## 6 11352.045 57213.15
## L_superior_frontal_gyrus_ShapeIndex L_superior_frontal_gyrus_Curvedness
## 1 0.3776485 0.05680316
## 2 0.3744766 0.05133303
## 3 0.3782946 0.05615867
## 4 0.3537120 0.05340476
## 5 0.3517075 0.05468875
## 6 0.3776325 0.05042987
## R_superior_frontal_gyrus_AvgMeanCurvature
## 1 0.09402456
## 2 0.08691916
## 3 0.08439535
## 4 0.08471537
## 5 0.07839364
## 6 0.08651596
## R_superior_frontal_gyrus_ComputeArea R_superior_frontal_gyrus_Volume
## 1 10022.785 46506.77
## 2 11134.168 52396.24
## 3 9314.438 40798.21
## 4 12392.188 63075.63
## 5 10432.360 50157.89
## 6 11086.727 54957.84
## R_superior_frontal_gyrus_ShapeIndex R_superior_frontal_gyrus_Curvedness
## 1 0.4016202 0.05349894
## 2 0.4484875 0.04586595
## 3 0.3865799 0.05608831
## 4 0.3838962 0.05540523
## 5 0.3818891 0.04603138
## 6 0.3856457 0.05724273
## L_middle_frontal_gyrus_AvgMeanCurvature
## 1 0.07402717
## 2 0.11952615
## 3 0.16637044
## 4 0.07611775
## 5 0.08236869
## 6 0.07264456
## L_middle_frontal_gyrus_ComputeArea L_middle_frontal_gyrus_Volume
## 1 9105.836 48109.75
## 2 9988.897 52452.81
## 3 8710.422 46543.30
## 4 9334.861 50114.31
## 5 8877.312 47018.99
## 6 9751.798 53668.02
## L_middle_frontal_gyrus_ShapeIndex L_middle_frontal_gyrus_Curvedness
## 1 0.3078132 0.07945989
## 2 0.3481521 0.06595413
## 3 0.3184140 0.08178205
## 4 0.3379107 0.05968499
## 5 0.3329076 0.06226124
## 6 0.2952311 0.71030190
## R_middle_frontal_gyrus_AvgMeanCurvature
## 1 0.07499459
## 2 0.07785072
## 3 0.07515226
## 4 0.07302976
## 5 0.08970708
## 6 0.07306901
## R_middle_frontal_gyrus_ComputeArea R_middle_frontal_gyrus_Volume
## 1 8851.557 49372.76
## 2 9660.306 54012.12
## 3 7882.520 42838.83
## 4 9610.101 55047.20
## 5 8067.214 43606.92
## 6 9913.557 57678.77
## R_middle_frontal_gyrus_ShapeIndex R_middle_frontal_gyrus_Curvedness
## 1 0.2965402 0.06233485
## 2 0.3178475 0.05932451
## 3 0.3149322 0.06536620
## 4 0.3090931 0.05702957
## 5 0.2863362 0.06827832
## 6 0.3256466 0.05722431
## L_inferior_frontal_gyrus_AvgMeanCurvature
## 1 0.09232418
## 2 0.08264828
## 3 0.09041177
## 4 0.08631535
## 5 0.08764468
## 6 0.07900866
## L_inferior_frontal_gyrus_ComputeArea L_inferior_frontal_gyrus_Volume
## 1 4781.623 21839.74
## 2 5091.727 25113.25
## 3 4106.199 17136.94
## 4 4556.781 20029.15
## 5 3760.532 15548.01
## 6 5508.372 27544.42
## L_inferior_frontal_gyrus_ShapeIndex L_inferior_frontal_gyrus_Curvedness
## 1 0.3050616 0.09872059
## 2 0.3067739 0.09213249
## 3 0.2913024 0.11054313
## 4 0.3102240 0.09227259
## 5 0.2875925 0.10289127
## 6 0.3188633 0.07897555
## R_inferior_frontal_gyrus_AvgMeanCurvature
## 1 0.08158166
## 2 0.07610716
## 3 0.08009501
## 4 0.08504603
## 5 0.08159838
## 6 0.07464060
## R_inferior_frontal_gyrus_ComputeArea R_inferior_frontal_gyrus_Volume
## 1 4948.708 24562.58
## 2 5612.835 28546.87
## 3 4664.688 22296.34
## 4 4665.766 21012.86
## 5 4851.100 23619.47
## 6 5576.678 28124.61
## R_inferior_frontal_gyrus_ShapeIndex R_inferior_frontal_gyrus_Curvedness
## 1 0.3293004 0.07844599
## 2 0.3206594 0.06914873
## 3 0.3192303 0.08402686
## 4 0.2894400 0.09034790
## 5 0.3095379 0.08705973
## 6 0.3361318 0.07954666
## L_precentral_gyrus_AvgMeanCurvature L_precentral_gyrus_ComputeArea
## 1 0.08822059 5945.166
## 2 0.09409574 6634.533
## 3 0.06789780 5764.879
## 4 0.08122137 6430.497
## 5 0.08871625 6380.224
## 6 0.09053335 6095.706
## L_precentral_gyrus_Volume L_precentral_gyrus_ShapeIndex
## 1 22275.78 0.3413521
## 2 25205.30 0.3518310
## 3 20273.68 0.3225783
## 4 24588.46 0.3580851
## 5 24206.99 0.3314704
## 6 21866.00 0.3506974
## L_precentral_gyrus_Curvedness R_precentral_gyrus_AvgMeanCurvature
## 1 0.09154550 0.08797655
## 2 0.09609143 0.08857492
## 3 0.10622379 0.09876766
## 4 0.08148415 0.08939573
## 5 0.08331495 0.09300800
## 6 0.07997279 0.08878694
## R_precentral_gyrus_ComputeArea R_precentral_gyrus_Volume
## 1 5820.268 23036.32
## 2 6057.209 23691.55
## 3 5726.844 21134.83
## 4 5780.951 21180.83
## 5 6269.956 25722.87
## 6 5792.416 21327.76
## R_precentral_gyrus_ShapeIndex R_precentral_gyrus_Curvedness
## 1 0.3609946 0.07470327
## 2 0.3654834 0.07582953
## 3 0.3747464 0.07726095
## 4 0.3623383 0.07459419
## 5 0.3483505 0.08644210
## 6 0.3695930 0.07515290
## L_middle_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.10756427
## 2 0.09504461
## 3 0.11195838
## 4 0.10857720
## 5 0.09857074
## 6 0.09876597
## L_middle_orbitofrontal_gyrus_ComputeArea
## 1 2959.834
## 2 3190.647
## 3 2643.407
## 4 2947.184
## 5 3115.858
## 6 2895.487
## L_middle_orbitofrontal_gyrus_Volume
## 1 8567.099
## 2 10459.919
## 3 7667.457
## 4 8872.217
## 5 9984.053
## 6 8819.313
## L_middle_orbitofrontal_gyrus_ShapeIndex
## 1 0.2946531
## 2 0.2909523
## 3 0.2836412
## 4 0.2768023
## 5 0.3300948
## 6 0.2772049
## L_middle_orbitofrontal_gyrus_Curvedness
## 1 0.09477899
## 2 0.08821871
## 3 0.10691605
## 4 0.08969987
## 5 0.07829051
## 6 0.09271120
## R_middle_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1192333
## 2 0.1093085
## 3 0.1086538
## 4 0.1020976
## 5 0.1122697
## 6 0.1085550
## R_middle_orbitofrontal_gyrus_ComputeArea
## 1 3188.606
## 2 3108.103
## 3 2871.688
## 4 3382.121
## 5 3031.104
## 6 3071.431
## R_middle_orbitofrontal_gyrus_Volume
## 1 9229.549
## 2 9611.130
## 3 8529.517
## 4 10460.593
## 5 8912.429
## 6 9319.833
## R_middle_orbitofrontal_gyrus_ShapeIndex
## 1 0.2755525
## 2 0.3060040
## 3 0.2800826
## 4 0.2821486
## 5 0.3010434
## 6 0.2772180
## R_middle_orbitofrontal_gyrus_Curvedness
## 1 0.09481500
## 2 0.09612964
## 3 0.09927428
## 4 0.09002830
## 5 0.10531771
## 6 0.09974456
## L_lateral_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1272921
## 2 0.1274801
## 3 0.1294754
## 4 0.1348371
## 5 0.1336065
## 6 0.1441287
## L_lateral_orbitofrontal_gyrus_ComputeArea
## 1 1953.602
## 2 1940.454
## 3 1705.715
## 4 1891.861
## 5 1771.899
## 6 2158.262
## L_lateral_orbitofrontal_gyrus_Volume
## 1 4038.399
## 2 4320.333
## 3 3392.857
## 4 4326.737
## 5 3627.903
## 6 4168.530
## L_lateral_orbitofrontal_gyrus_ShapeIndex
## 1 0.2902372
## 2 0.2895921
## 3 0.2407804
## 4 0.3053100
## 5 0.3058705
## 6 0.2848462
## L_lateral_orbitofrontal_gyrus_Curvedness
## 1 0.1766418
## 2 0.1775847
## 3 0.1863002
## 4 0.1467295
## 5 0.1678090
## 6 0.1479501
## R_lateral_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1309167
## 2 0.1397253
## 3 0.1562820
## 4 0.1467773
## 5 0.1434309
## 6 0.1299206
## R_lateral_orbitofrontal_gyrus_ComputeArea
## 1 1705.245
## 2 1953.675
## 3 1889.294
## 4 1708.332
## 5 1831.184
## 6 1982.338
## R_lateral_orbitofrontal_gyrus_Volume
## 1 3655.639
## 2 4093.492
## 3 3713.498
## 4 3450.264
## 5 3699.269
## 6 4330.369
## R_lateral_orbitofrontal_gyrus_ShapeIndex
## 1 0.2628306
## 2 0.2971379
## 3 0.2971216
## 4 0.2777466
## 5 0.2543070
## 6 0.2892046
## R_lateral_orbitofrontal_gyrus_Curvedness L_gyrus_rectus_AvgMeanCurvature
## 1 0.1874613 0.1964838
## 2 0.1634671 0.2078852
## 3 0.1559738 0.2408347
## 4 0.1939476 0.1516295
## 5 0.1342819 0.2300583
## 6 0.1474630 0.2162944
## L_gyrus_rectus_ComputeArea L_gyrus_rectus_Volume
## 1 980.4423 1619.7333
## 2 1143.0779 1785.8225
## 3 690.5019 974.7885
## 4 1360.0150 2701.2750
## 5 972.7225 1495.7976
## 6 908.7805 1437.0935
## L_gyrus_rectus_ShapeIndex L_gyrus_rectus_Curvedness
## 1 0.2539865 0.2170866
## 2 0.2893511 0.2222131
## 3 0.2537049 0.3158460
## 4 0.2726588 0.1739792
## 5 0.2888985 0.2187143
## 6 0.2653972 0.2614590
## R_gyrus_rectus_AvgMeanCurvature R_gyrus_rectus_ComputeArea
## 1 0.1471301 1411.747
## 2 0.1519435 1408.899
## 3 0.1727085 1099.657
## 4 0.1333407 1501.545
## 5 0.1937611 1171.779
## 6 0.1778007 1229.734
## R_gyrus_rectus_Volume R_gyrus_rectus_ShapeIndex
## 1 2781.022 0.2794396
## 2 2763.942 0.2612030
## 3 1962.041 0.2749456
## 4 3253.902 0.2725946
## 5 1957.030 0.2926626
## 6 2222.924 0.2696461
## R_gyrus_rectus_Curvedness L_postcentral_gyrus_AvgMeanCurvature
## 1 0.1832981 0.10508093
## 2 0.1959469 0.09087086
## 3 0.1815064 0.10574263
## 4 0.1462237 0.09284356
## 5 0.2194676 0.11175828
## 6 0.1897803 0.09467665
## L_postcentral_gyrus_ComputeArea L_postcentral_gyrus_Volume
## 1 5852.116 21565.38
## 2 5952.575 22426.55
## 3 6141.619 22325.22
## 4 5269.063 17683.91
## 5 6435.081 22275.72
## 6 5747.097 19637.25
## L_postcentral_gyrus_ShapeIndex L_postcentral_gyrus_Curvedness
## 1 0.3432217 0.09591017
## 2 0.3305127 0.08240747
## 3 0.3112023 0.08712497
## 4 0.3287937 0.09031591
## 5 0.3179572 0.10079832
## 6 0.3216686 0.07746661
## R_postcentral_gyrus_AvgMeanCurvature R_postcentral_gyrus_ComputeArea
## 1 0.09829028 5843.220
## 2 0.07717761 6274.401
## 3 0.08963424 5729.988
## 4 0.44033816 6292.385
## 5 0.10047813 7229.333
## 6 0.09394788 5858.971
## R_postcentral_gyrus_Volume R_postcentral_gyrus_ShapeIndex
## 1 21663.67 0.3266972
## 2 26116.10 0.3320086
## 3 18710.21 0.3128947
## 4 25337.88 0.3112892
## 5 30405.49 0.2807297
## 6 20709.22 0.3246994
## R_postcentral_gyrus_Curvedness
## 1 0.09283601
## 2 0.08080232
## 3 0.08389504
## 4 0.09159105
## 5 0.09136557
## 6 0.07954769
## L_superior_parietal_gyrus_AvgMeanCurvature
## 1 0.10138974
## 2 0.15642601
## 3 0.07739609
## 4 0.08403327
## 5 0.08048810
## 6 0.08444352
## L_superior_parietal_gyrus_ComputeArea L_superior_parietal_gyrus_Volume
## 1 5358.074 25996.28
## 2 5228.112 22835.86
## 3 4866.638 22815.78
## 4 4257.462 17449.40
## 5 5282.335 23962.46
## 6 5567.526 25985.85
## L_superior_parietal_gyrus_ShapeIndex
## 1 0.2905781
## 2 0.3180919
## 3 0.2827858
## 4 0.2894923
## 5 0.2729865
## 6 0.2984420
## L_superior_parietal_gyrus_Curvedness
## 1 0.09668629
## 2 0.08668642
## 3 0.09646292
## 4 0.09863686
## 5 0.09134870
## 6 0.08186932
## R_superior_parietal_gyrus_AvgMeanCurvature
## 1 0.08267691
## 2 0.07807680
## 3 0.07335306
## 4 0.08272585
## 5 0.08455538
## 6 0.07725251
## R_superior_parietal_gyrus_ComputeArea R_superior_parietal_gyrus_Volume
## 1 5673.109 28109.21
## 2 5121.035 25387.02
## 3 5449.289 28970.75
## 4 4690.151 20883.88
## 5 5704.847 28112.53
## 6 5932.755 30813.30
## R_superior_parietal_gyrus_ShapeIndex
## 1 0.2624184
## 2 0.2799022
## 3 0.2819246
## 4 0.2475126
## 5 0.2767437
## 6 0.2791106
## R_superior_parietal_gyrus_Curvedness
## 1 0.08636291
## 2 0.08711799
## 3 0.08845111
## 4 0.09899254
## 5 0.09276057
## 6 0.09179036
## L_supramarginal_gyrus_AvgMeanCurvature L_supramarginal_gyrus_ComputeArea
## 1 0.10292991 3257.416
## 2 0.08960319 3766.590
## 3 0.09134826 3766.664
## 4 0.09775341 3466.433
## 5 0.10188387 3980.114
## 6 0.08835363 3835.389
## L_supramarginal_gyrus_Volume L_supramarginal_gyrus_ShapeIndex
## 1 12039.86 0.2978995
## 2 14904.22 0.3068543
## 3 15181.61 0.2855105
## 4 13164.55 0.3096961
## 5 16108.37 0.3027252
## 6 15204.89 0.2790939
## L_supramarginal_gyrus_Curvedness R_supramarginal_gyrus_AvgMeanCurvature
## 1 0.10960597 0.09751207
## 2 0.09030263 0.09359056
## 3 0.10938548 0.10048632
## 4 0.10245745 0.06148638
## 5 0.09792726 0.08745759
## 6 0.10536623 0.09508114
## R_supramarginal_gyrus_ComputeArea R_supramarginal_gyrus_Volume
## 1 2875.999 11212.458
## 2 2943.982 11236.216
## 3 2397.780 8306.699
## 4 2804.045 8747.607
## 5 3323.411 11621.903
## 6 3334.477 12910.579
## R_supramarginal_gyrus_ShapeIndex R_supramarginal_gyrus_Curvedness
## 1 0.2874430 0.1262914
## 2 0.3043556 0.1132410
## 3 0.2951722 0.1185719
## 4 0.2294821 0.1899408
## 5 0.2680114 0.1379686
## 6 0.2954146 0.1125512
## L_angular_gyrus_AvgMeanCurvature L_angular_gyrus_ComputeArea
## 1 0.09211609 3518.132
## 2 0.08332833 4499.945
## 3 0.08921783 3811.192
## 4 0.07404549 3802.423
## 5 0.08713398 4175.902
## 6 0.08054429 4541.701
## L_angular_gyrus_Volume L_angular_gyrus_ShapeIndex
## 1 13911.83 0.2931503
## 2 18707.85 0.3069968
## 3 15449.27 0.2856840
## 4 15203.80 0.3085006
## 5 16791.69 0.2809210
## 6 20320.15 0.2977993
## L_angular_gyrus_Curvedness R_angular_gyrus_AvgMeanCurvature
## 1 0.12313346 0.08108542
## 2 0.08690669 0.10242639
## 3 0.10374778 0.08759734
## 4 0.12590794 0.09118984
## 5 0.10382951 0.07759675
## 6 0.09659023 0.09858398
## R_angular_gyrus_ComputeArea R_angular_gyrus_Volume
## 1 3752.691 13254.46
## 2 3666.847 13003.38
## 3 3912.730 15305.92
## 4 4120.223 16804.60
## 5 3788.444 12779.83
## 6 4638.663 17428.22
## R_angular_gyrus_ShapeIndex R_angular_gyrus_Curvedness
## 1 0.2942332 0.11413383
## 2 0.2994884 0.11061017
## 3 0.2862641 0.11115348
## 4 0.3036556 0.09627947
## 5 0.2554384 0.14470702
## 6 0.2893637 0.09096058
## L_precuneus_AvgMeanCurvature L_precuneus_ComputeArea L_precuneus_Volume
## 1 0.09815007 3352.910 10894.338
## 2 0.10264540 3065.234 9302.229
## 3 0.10856181 2888.769 8250.564
## 4 0.10954109 3356.718 10437.275
## 5 0.09576278 3383.793 10492.146
## 6 0.10161209 3404.712 10243.525
## L_precuneus_ShapeIndex L_precuneus_Curvedness
## 1 0.2832698 0.10701959
## 2 0.3086211 0.09695860
## 3 0.2862202 0.11096862
## 4 0.2799505 0.09913154
## 5 0.2971880 0.09365830
## 6 0.2842773 0.08811536
## R_precuneus_AvgMeanCurvature R_precuneus_ComputeArea R_precuneus_Volume
## 1 0.09441046 3387.310 12464.342
## 2 0.10789248 3332.431 11293.901
## 3 0.13150418 2799.881 8080.517
## 4 0.10829975 3435.362 11166.362
## 5 0.09613719 3556.810 11808.917
## 6 0.09127127 4114.963 14350.119
## R_precuneus_ShapeIndex R_precuneus_Curvedness
## 1 0.2879712 0.09666886
## 2 0.3032337 0.09921845
## 3 0.3310782 0.11408704
## 4 0.3263887 0.09054042
## 5 0.2982102 0.09661751
## 6 0.3037840 0.08448501
## L_superior_occipital_gyrus_AvgMeanCurvature
## 1 0.1195798
## 2 0.1205780
## 3 0.1129942
## 4 0.1046885
## 5 0.1191842
## 6 0.1181061
## L_superior_occipital_gyrus_ComputeArea L_superior_occipital_gyrus_Volume
## 1 2561.545 7294.012
## 2 1909.412 5150.974
## 3 2008.023 5697.149
## 4 2412.866 7512.356
## 5 2060.643 5664.248
## 6 1854.029 5149.896
## L_superior_occipital_gyrus_ShapeIndex
## 1 0.2949458
## 2 0.2719930
## 3 0.2651365
## 4 0.2711364
## 5 0.3079957
## 6 0.2666291
## L_superior_occipital_gyrus_Curvedness
## 1 0.1393213
## 2 0.1500819
## 3 0.1310171
## 4 0.1362769
## 5 0.1426280
## 6 0.1456626
## R_superior_occipital_gyrus_AvgMeanCurvature
## 1 0.1087897
## 2 0.1171069
## 3 0.1037152
## 4 0.1320050
## 5 0.1404328
## 6 0.1168706
## R_superior_occipital_gyrus_ComputeArea R_superior_occipital_gyrus_Volume
## 1 2564.923 6882.760
## 2 2173.619 6231.256
## 3 2579.026 7324.230
## 4 2079.545 5355.727
## 5 1893.186 4682.705
## 6 2202.703 5808.980
## R_superior_occipital_gyrus_ShapeIndex
## 1 0.2609457
## 2 0.2585772
## 3 0.2641015
## 4 0.3012312
## 5 0.2889157
## 6 0.2803689
## R_superior_occipital_gyrus_Curvedness
## 1 0.1488038
## 2 0.1520477
## 3 0.1623094
## 4 0.1615290
## 5 0.1520513
## 6 0.1563103
## L_middle_occipital_gyrus_AvgMeanCurvature
## 1 0.09196750
## 2 0.08442675
## 3 0.09127597
## 4 0.08336854
## 5 0.09361627
## 6 0.09248868
## L_middle_occipital_gyrus_ComputeArea L_middle_occipital_gyrus_Volume
## 1 4264.960 15722.27
## 2 4503.611 17128.71
## 3 3955.185 14555.55
## 4 4740.876 19821.23
## 5 4229.802 15466.30
## 6 4528.289 16743.73
## L_middle_occipital_gyrus_ShapeIndex L_middle_occipital_gyrus_Curvedness
## 1 0.3160000 0.09652114
## 2 0.3417904 0.08082197
## 3 0.3040396 0.08682547
## 4 0.2885401 0.09235392
## 5 0.3201293 0.09023690
## 6 0.3066691 0.08634961
## R_middle_occipital_gyrus_AvgMeanCurvature
## 1 0.09389006
## 2 0.09667509
## 3 0.09152445
## 4 0.09479980
## 5 0.09341706
## 6 0.09855936
## R_middle_occipital_gyrus_ComputeArea R_middle_occipital_gyrus_Volume
## 1 4827.668 17938.67
## 2 5311.343 20127.27
## 3 4945.606 19244.09
## 4 4430.510 16604.35
## 5 4372.664 15408.93
## 6 5884.962 21090.49
## R_middle_occipital_gyrus_ShapeIndex R_middle_occipital_gyrus_Curvedness
## 1 0.3035519 0.09360675
## 2 0.3181087 0.08639906
## 3 0.3171960 0.09104467
## 4 0.3185526 0.09902467
## 5 0.2987557 0.09306130
## 6 0.3683309 0.07721975
## L_inferior_occipital_gyrus_AvgMeanCurvature
## 1 0.1157437
## 2 0.1102742
## 3 0.1137141
## 4 0.1133252
## 5 0.1071762
## 6 0.1132877
## L_inferior_occipital_gyrus_ComputeArea L_inferior_occipital_gyrus_Volume
## 1 2518.832 7449.263
## 2 2575.385 7956.538
## 3 2371.204 6598.951
## 4 2990.778 9397.859
## 5 2649.330 8162.506
## 6 2613.085 8295.724
## L_inferior_occipital_gyrus_ShapeIndex
## 1 0.3156508
## 2 0.3179899
## 3 0.3162082
## 4 0.2986863
## 5 0.2859327
## 6 0.3054663
## L_inferior_occipital_gyrus_Curvedness
## 1 0.1125443
## 2 0.1061467
## 3 0.0936433
## 4 0.1164720
## 5 0.1064660
## 6 0.1089161
## R_inferior_occipital_gyrus_AvgMeanCurvature
## 1 0.09804650
## 2 0.08905351
## 3 0.09975755
## 4 0.10061581
## 5 0.09822466
## 6 0.11223317
## R_inferior_occipital_gyrus_ComputeArea R_inferior_occipital_gyrus_Volume
## 1 2984.339 10226.646
## 2 3322.390 12708.507
## 3 2778.404 9224.226
## 4 2851.548 9282.553
## 5 3010.978 10378.511
## 6 2944.898 9461.373
## R_inferior_occipital_gyrus_ShapeIndex
## 1 0.3049062
## 2 0.3491609
## 3 0.3147194
## 4 0.3003561
## 5 0.3038200
## 6 0.3234459
## R_inferior_occipital_gyrus_Curvedness L_cuneus_AvgMeanCurvature
## 1 0.08907501 0.1156776
## 2 0.07681380 0.1215609
## 3 0.08843280 0.1153973
## 4 0.09368360 0.1151997
## 5 0.09392322 0.1259525
## 6 0.09704642 0.1202144
## L_cuneus_ComputeArea L_cuneus_Volume L_cuneus_ShapeIndex
## 1 3138.689 9618.492 0.3003197
## 2 2430.494 6097.885 0.3004880
## 3 2313.170 6014.666 0.2840494
## 4 2319.499 5829.940 0.2748422
## 5 2261.542 5420.857 0.2868048
## 6 2458.758 5870.765 0.2983612
## L_cuneus_Curvedness R_cuneus_AvgMeanCurvature R_cuneus_ComputeArea
## 1 0.1077938 0.10219094 3519.513
## 2 0.1215284 0.10529532 2722.789
## 3 0.1151975 0.11698650 2621.120
## 4 0.1400617 0.11343918 2494.251
## 5 0.1352942 0.10537895 2782.892
## 6 0.1222192 0.08870164 2762.852
## R_cuneus_Volume R_cuneus_ShapeIndex R_cuneus_Curvedness
## 1 11940.071 0.3023419 0.1016638
## 2 8342.228 0.2938111 0.1017114
## 3 7704.615 0.2649785 0.1338569
## 4 7534.842 0.2746295 0.1285215
## 5 8066.804 0.2726736 0.1171792
## 6 7842.051 0.2344456 0.1397076
## L_superior_temporal_gyrus_AvgMeanCurvature
## 1 0.08533024
## 2 0.08818216
## 3 0.09173026
## 4 0.07776233
## 5 0.08349276
## 6 0.08078913
## L_superior_temporal_gyrus_ComputeArea L_superior_temporal_gyrus_Volume
## 1 6635.644 26860.47
## 2 7677.646 32329.76
## 3 6158.281 23263.73
## 4 6995.225 29718.22
## 5 6772.355 26337.94
## 6 7671.939 32911.09
## L_superior_temporal_gyrus_ShapeIndex
## 1 0.3783706
## 2 0.3832396
## 3 0.3619839
## 4 0.3903924
## 5 0.3598613
## 6 0.3658588
## L_superior_temporal_gyrus_Curvedness
## 1 0.07048535
## 2 0.06207907
## 3 0.07330941
## 4 0.06313585
## 5 0.06630546
## 6 0.06012147
## R_superior_temporal_gyrus_AvgMeanCurvature
## 1 0.08869901
## 2 0.08121426
## 3 0.09115610
## 4 0.08373479
## 5 0.08799037
## 6 0.08795319
## R_superior_temporal_gyrus_ComputeArea R_superior_temporal_gyrus_Volume
## 1 5995.017 24386.22
## 2 6343.222 26888.90
## 3 5446.194 20156.59
## 4 6260.992 25252.26
## 5 5778.582 22021.75
## 6 6905.909 26694.02
## R_superior_temporal_gyrus_ShapeIndex
## 1 0.3649489
## 2 0.3733774
## 3 0.3470211
## 4 0.3920464
## 5 0.3671237
## 6 0.3715455
## R_superior_temporal_gyrus_Curvedness
## 1 0.06542411
## 2 0.06523898
## 3 0.07907334
## 4 0.05600856
## 5 0.06634440
## 6 0.06216614
## L_middle_temporal_gyrus_AvgMeanCurvature
## 1 0.13577196
## 2 0.10573145
## 3 0.12981117
## 4 0.09540415
## 5 0.10457954
## 6 0.10548477
## L_middle_temporal_gyrus_ComputeArea L_middle_temporal_gyrus_Volume
## 1 5233.565 16947.69
## 2 5470.255 17208.84
## 3 5325.748 17144.86
## 4 5574.003 16750.46
## 5 5358.926 15552.00
## 6 6736.122 24755.54
## L_middle_temporal_gyrus_ShapeIndex L_middle_temporal_gyrus_Curvedness
## 1 0.2918232 0.13046893
## 2 0.3291948 0.08871739
## 3 0.2944165 0.09009954
## 4 0.3190153 0.08547634
## 5 0.3192232 0.08140095
## 6 0.3021110 0.08742789
## R_middle_temporal_gyrus_AvgMeanCurvature
## 1 0.09881832
## 2 0.09784834
## 3 0.09662573
## 4 0.09304032
## 5 0.11314295
## 6 0.09616611
## R_middle_temporal_gyrus_ComputeArea R_middle_temporal_gyrus_Volume
## 1 6130.960 22232.61
## 2 6960.965 27476.33
## 3 5840.725 20638.79
## 4 7021.523 27743.97
## 5 6097.444 21165.43
## 6 7028.058 25797.53
## R_middle_temporal_gyrus_ShapeIndex R_middle_temporal_gyrus_Curvedness
## 1 0.3372119 0.07372350
## 2 0.3180938 0.07262339
## 3 0.2952768 0.08719198
## 4 0.3164606 0.07635997
## 5 0.3289957 0.08069239
## 6 0.3185178 0.07104691
## L_inferior_temporal_gyrus_AvgMeanCurvature
## 1 0.08387626
## 2 0.08440291
## 3 0.15046303
## 4 0.08386965
## 5 0.08181475
## 6 0.08854481
## L_inferior_temporal_gyrus_ComputeArea L_inferior_temporal_gyrus_Volume
## 1 5425.194 21706.26
## 2 5925.988 24980.90
## 3 5455.426 21813.93
## 4 6033.020 25337.60
## 5 5764.396 24265.72
## 6 6161.727 24965.63
## L_inferior_temporal_gyrus_ShapeIndex
## 1 0.3529576
## 2 0.3863400
## 3 0.3633563
## 4 0.3818763
## 5 0.3801266
## 6 0.3633352
## L_inferior_temporal_gyrus_Curvedness
## 1 0.08126605
## 2 0.06998834
## 3 0.08530215
## 4 0.06878814
## 5 0.06386685
## 6 0.07705876
## R_inferior_temporal_gyrus_AvgMeanCurvature
## 1 0.09733136
## 2 0.14309865
## 3 0.09224883
## 4 0.09705139
## 5 0.09015700
## 6 0.09348744
## R_inferior_temporal_gyrus_ComputeArea R_inferior_temporal_gyrus_Volume
## 1 4889.915 16930.42
## 2 5113.919 18076.97
## 3 4968.181 18178.27
## 4 5406.621 18588.60
## 5 4977.797 18143.35
## 6 6347.293 23908.35
## R_inferior_temporal_gyrus_ShapeIndex
## 1 0.3653207
## 2 0.3730978
## 3 0.3642670
## 4 0.3726786
## 5 0.3547782
## 6 0.3643752
## R_inferior_temporal_gyrus_Curvedness
## 1 0.07721288
## 2 0.09922728
## 3 0.07650701
## 4 0.07453465
## 5 0.07617285
## 6 0.07768165
## L_parahippocampal_gyrus_AvgMeanCurvature
## 1 0.1058454
## 2 0.1077122
## 3 0.1155507
## 4 0.1155695
## 5 0.1051995
## 6 0.1090259
## L_parahippocampal_gyrus_ComputeArea L_parahippocampal_gyrus_Volume
## 1 2575.285 7266.162
## 2 2844.754 7480.951
## 3 2255.258 5632.681
## 4 2282.834 5927.611
## 5 2486.467 6690.719
## 6 3000.342 8537.208
## L_parahippocampal_gyrus_ShapeIndex L_parahippocampal_gyrus_Curvedness
## 1 0.2775562 0.1167113
## 2 0.2607401 0.1022136
## 3 0.2680552 0.1288539
## 4 0.2918659 0.1136002
## 5 0.2689256 0.1214095
## 6 0.2985118 0.1031911
## R_parahippocampal_gyrus_AvgMeanCurvature
## 1 0.09920542
## 2 0.10265286
## 3 0.10990323
## 4 0.10331920
## 5 0.10237843
## 6 0.10002774
## R_parahippocampal_gyrus_ComputeArea R_parahippocampal_gyrus_Volume
## 1 2847.448 8022.121
## 2 3198.464 9512.674
## 3 2831.426 7244.603
## 4 2852.929 7751.758
## 5 2909.676 8168.243
## 6 4216.187 12961.197
## R_parahippocampal_gyrus_ShapeIndex R_parahippocampal_gyrus_Curvedness
## 1 0.2625498 0.10588548
## 2 0.3130394 0.09406152
## 3 0.2706445 0.11459741
## 4 0.2843905 0.11661233
## 5 0.2913733 0.10670050
## 6 0.2892527 0.08910739
## L_lingual_gyrus_AvgMeanCurvature L_lingual_gyrus_ComputeArea
## 1 0.09325381 4064.914
## 2 0.09506937 3990.686
## 3 0.10162731 3147.679
## 4 0.08526740 4111.653
## 5 0.09714551 3414.919
## 6 0.09066825 3527.309
## L_lingual_gyrus_Volume L_lingual_gyrus_ShapeIndex
## 1 15295.09 0.3398995
## 2 15329.18 0.3268375
## 3 10175.86 0.3332995
## 4 15891.62 0.3302158
## 5 11521.30 0.3171585
## 6 12886.17 0.3350441
## L_lingual_gyrus_Curvedness R_lingual_gyrus_AvgMeanCurvature
## 1 0.07595673 0.08326412
## 2 0.08228135 0.08600172
## 3 0.08935409 0.09665706
## 4 0.07737984 0.09421878
## 5 0.08903558 0.09192208
## 6 0.08423320 0.09375447
## R_lingual_gyrus_ComputeArea R_lingual_gyrus_Volume
## 1 4296.131 17798.51
## 2 4208.788 16959.60
## 3 3310.970 12100.64
## 4 3764.841 14056.34
## 5 3714.116 13833.59
## 6 3956.547 15209.61
## R_lingual_gyrus_ShapeIndex R_lingual_gyrus_Curvedness
## 1 0.3224243 0.07223333
## 2 0.3203310 0.07572543
## 3 0.3205029 0.08572730
## 4 0.2945016 0.09222344
## 5 0.3098974 0.09112668
## 6 0.3258160 0.08625550
## L_fusiform_gyrus_AvgMeanCurvature L_fusiform_gyrus_ComputeArea
## 1 0.10196499 4239.945
## 2 0.10385320 4534.707
## 3 0.10405280 4013.385
## 4 0.11326566 3902.592
## 5 0.11218079 4120.621
## 6 0.09365524 4864.410
## L_fusiform_gyrus_Volume L_fusiform_gyrus_ShapeIndex
## 1 14728.60 0.3083931
## 2 15830.32 0.3102792
## 3 12677.99 0.2857870
## 4 11525.38 0.2983204
## 5 13775.00 0.3584661
## 6 17467.13 0.2929722
## L_fusiform_gyrus_Curvedness R_fusiform_gyrus_AvgMeanCurvature
## 1 0.08787598 0.1003429
## 2 0.08341972 0.1063175
## 3 0.10808808 0.1267130
## 4 0.09976534 0.1076625
## 5 0.09042557 0.1018607
## 6 0.09020279 0.1199351
## R_fusiform_gyrus_ComputeArea R_fusiform_gyrus_Volume
## 1 3934.233 13339.14
## 2 3945.037 14471.84
## 3 3551.876 11263.23
## 4 4260.940 14213.62
## 5 3919.842 14405.84
## 6 5169.535 17455.32
## R_fusiform_gyrus_ShapeIndex R_fusiform_gyrus_Curvedness Sex Weight
## 1 0.3381375 0.08972141 2 64.0
## 2 0.3592094 0.07762280 1 74.2
## 3 0.3331986 0.10994359 2 70.6
## 4 0.3515383 0.08235058 2 82.0
## 5 0.3487578 0.09018205 1 81.6
## 6 0.3855645 0.07418299 2 69.1
## ResearchGroup VisitID Age chr12_rs34637584_GT chr12_rs34637584_DP
## 1 Control 1 69.1781 0 24
## 2 PD 1 65.1808 0 23
## 3 PD 1 67.6247 0 24
## 4 PD 1 56.7562 0 23
## 5 Control 1 59.4548 0 24
## 6 PD 1 57.5781 0 20
## chr12_rs34637584_GQ chr17_rs11868035_GT chr17_rs11868035_DP
## 1 66 1 70
## 2 69 1 53
## 3 60 1 67
## 4 60 1 106
## 5 60 1 120
## 6 60 0 31
## chr17_rs11868035_GQ chr17_rs11012_GT chr17_rs11012_DP chr17_rs11012_GQ
## 1 99 1 55 99
## 2 99 1 60 99
## 3 99 0 26 66
## 4 99 1 70 99
## 5 99 1 67 99
## 6 70 1 41 99
## chr17_rs393152_GT chr17_rs393152_DP chr17_rs393152_GQ
## 1 1 27 99
## 2 1 15 99
## 3 0 28 78
## 4 1 23 77
## 5 1 30 99
## 6 1 9 33
## chr17_rs12185268_GT chr17_rs12185268_DP chr17_rs12185268_GQ
## 1 1 68 99
## 2 1 65 99
## 3 0 27 66
## 4 1 69 99
## 5 1 76 99
## 6 1 47 99
## chr17_rs199533_GT chr17_rs199533_DP chr17_rs199533_GQ
## 1 1 59 99
## 2 0 20 60
## 3 0 26 66
## 4 1 65 99
## 5 1 55 99
## 6 1 33 99
## chr12_rs34637584_AD_1 chr12_rs34637584_AD_2 chr17_rs11868035_AD_1
## 1 24 0 28
## 2 23 0 32
## 3 24 0 32
## 4 23 0 58
## 5 24 0 64
## 6 20 0 31
## chr17_rs11868035_AD_2 chr17_rs11012_AD_1 chr17_rs11012_AD_2
## 1 42 23 32
## 2 21 32 28
## 3 35 26 0
## 4 48 24 46
## 5 56 32 35
## 6 0 19 22
## chr17_rs393152_AD_1 chr17_rs393152_AD_2 chr17_rs12185268_AD_1
## 1 16 11 41
## 2 10 5 27
## 3 28 0 27
## 4 5 18 35
## 5 21 9 53
## 6 2 7 29
## chr17_rs12185268_AD_2 chr17_rs199533_AD_1 chr17_rs199533_AD_2
## 1 27 24 35
## 2 38 20 0
## 3 0 26 0
## 4 34 33 32
## 5 23 31 24
## 6 18 17 16
## chr12_rs34637584_PL_1 chr12_rs34637584_PL_2 chr12_rs34637584_PL_3
## 1 0 66 681
## 2 0 69 726
## 3 0 60 759
## 4 0 60 857
## 5 0 60 762
## 6 0 60 587
## chr17_rs11868035_PL_1 chr17_rs11868035_PL_2 chr17_rs11868035_PL_3
## 1 982 0 645
## 2 498 0 848
## 3 870 0 809
## 4 1028 0 1504
## 5 1219 0 1636
## 6 0 70 936
## chr17_rs11012_PL_1 chr17_rs11012_PL_2 chr17_rs11012_PL_3
## 1 737 0 549
## 2 679 0 735
## 3 0 66 990
## 4 1125 0 494
## 5 839 0 678
## 6 511 0 485
## chr17_rs393152_PL_1 chr17_rs393152_PL_2 chr17_rs393152_PL_3
## 1 249 0 372
## 2 140 0 242
## 3 0 78 809
## 4 437 0 77
## 5 168 0 529
## 6 201 0 33
## chr17_rs12185268_PL_1 chr17_rs12185268_PL_2 chr17_rs12185268_PL_3
## 1 590 0 971
## 2 1013 0 616
## 3 0 66 903
## 4 809 0 837
## 5 499 0 1310
## 6 380 0 672
## chr17_rs199533_PL_1 chr17_rs199533_PL_2 chr17_rs199533_PL_3
## 1 939 0 545
## 2 0 60 709
## 3 0 66 847
## 4 810 0 925
## 5 556 0 796
## 6 386 0 408
## UPDRS_Part_I_Summary_Score_Baseline UPDRS_Part_I_Summary_Score_Month_03
## 1 NA NA
## 2 0 0
## 3 3 2
## 4 1 1
## 5 0 NA
## 6 1 2
## UPDRS_Part_I_Summary_Score_Month_06 UPDRS_Part_I_Summary_Score_Month_09
## 1 NA NA
## 2 0 1
## 3 NA 1
## 4 0 0
## 5 NA NA
## 6 0 1
## UPDRS_Part_I_Summary_Score_Month_12 UPDRS_Part_I_Summary_Score_Month_18
## 1 NA NA
## 2 4 1
## 3 3 4
## 4 0 NA
## 5 0 NA
## 6 2 NA
## UPDRS_Part_I_Summary_Score_Month_24 UPDRS_Part_I_Summary_Score_Month_30
## 1 NA NA
## 2 0 0
## 3 8 4
## 4 0 3
## 5 1 NA
## 6 NA NA
## UPDRS_Part_I_Summary_Score_Month_36 UPDRS_Part_I_Summary_Score_Month_42
## 1 NA NA
## 2 1 1
## 3 4 6
## 4 2 0
## 5 1 NA
## 6 NA NA
## UPDRS_Part_I_Summary_Score_Month_48 UPDRS_Part_I_Summary_Score_Month_54
## 1 NA NA
## 2 1 NA
## 3 5 NA
## 4 0 NA
## 5 0 NA
## 6 NA NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline
## 1 NA
## 2 2
## 3 15
## 4 6
## 5 0
## 6 4
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_03
## 1 NA
## 2 2
## 3 10
## 4 5
## 5 NA
## 6 10
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_06
## 1 NA
## 2 3
## 3 NA
## 4 3
## 5 NA
## 6 10
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_09
## 1 NA
## 2 3
## 3 16
## 4 8
## 5 NA
## 6 8
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_12
## 1 NA
## 2 3
## 3 15
## 4 5
## 5 0
## 6 13
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_18
## 1 NA
## 2 4
## 3 16
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_24
## 1 NA
## 2 2
## 3 14
## 4 7
## 5 0
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_30
## 1 NA
## 2 5
## 3 13
## 4 7
## 5 NA
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_36
## 1 NA
## 2 6
## 3 16
## 4 10
## 5 0
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_42
## 1 NA
## 2 11
## 3 14
## 4 13
## 5 NA
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_48
## 1 NA
## 2 5
## 3 18
## 4 10
## 5 0
## 6 NA
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Month_54
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_III_Summary_Score_Baseline
## 1 NA
## 2 12
## 3 17
## 4 29
## 5 2
## 6 22
## UPDRS_Part_III_Summary_Score_Month_03
## 1 NA
## 2 18
## 3 22
## 4 37
## 5 NA
## 6 30
## UPDRS_Part_III_Summary_Score_Month_06
## 1 NA
## 2 23
## 3 NA
## 4 37
## 5 NA
## 6 31
## UPDRS_Part_III_Summary_Score_Month_09
## 1 NA
## 2 19
## 3 20
## 4 33
## 5 NA
## 6 32
## UPDRS_Part_III_Summary_Score_Month_12
## 1 NA
## 2 20
## 3 27
## 4 44
## 5 3
## 6 33
## UPDRS_Part_III_Summary_Score_Month_18
## 1 NA
## 2 29
## 3 22
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_24
## 1 NA
## 2 39
## 3 22
## 4 43
## 5 7
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_30
## 1 NA
## 2 25
## 3 24
## 4 37
## 5 NA
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_36
## 1 NA
## 2 34
## 3 31
## 4 46
## 5 6
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_42
## 1 NA
## 2 42
## 3 19
## 4 47
## 5 NA
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_48
## 1 NA
## 2 39
## 3 29
## 4 59
## 5 1
## 6 NA
## UPDRS_Part_III_Summary_Score_Month_54
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Baseline
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_03
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 0
## UPDRS_Part_IV_Summary_Score_Month_06
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_09
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 0
## UPDRS_Part_IV_Summary_Score_Month_12
## 1 NA
## 2 NA
## 3 0
## 4 NA
## 5 NA
## 6 0
## UPDRS_Part_IV_Summary_Score_Month_18
## 1 NA
## 2 NA
## 3 0
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_24
## 1 NA
## 2 NA
## 3 0
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_30
## 1 NA
## 2 NA
## 3 1
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_36
## 1 NA
## 2 NA
## 3 4
## 4 NA
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_42
## 1 NA
## 2 0
## 3 4
## 4 0
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_48
## 1 NA
## 2 0
## 3 3
## 4 0
## 5 NA
## 6 NA
## UPDRS_Part_IV_Summary_Score_Month_54
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline
## 1 NA
## 2 6
## 3 14
## 4 8
## 5 6
## 6 0
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_06
## 1 NA
## 2 7
## 3 NA
## 4 8
## 5 NA
## 6 0
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_09
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_12
## 1 NA
## 2 3
## 3 14
## 4 6
## 5 6
## 6 6
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_18
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_24
## 1 NA
## 2 7
## 3 11
## 4 7
## 5 10
## 6 NA
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_36
## 1 NA
## 2 5
## 3 14
## 4 7
## 5 11
## 6 NA
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Month_48
## 1 NA
## 2 6
## 3 17
## 4 6
## 5 10
## 6 NA
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline
## 1 NA
## 2 6
## 3 6
## 4 4
## 5 5
## 6 5
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_06
## 1 NA
## 2 4
## 3 NA
## 4 4
## 5 NA
## 6 5
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_09
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_12
## 1 NA
## 2 5
## 3 6
## 4 4
## 5 4
## 6 5
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_18
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_24
## 1 NA
## 2 5
## 3 9
## 4 4
## 5 6
## 6 NA
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_36
## 1 NA
## 2 7
## 3 6
## 4 4
## 5 5
## 6 NA
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Month_48
## 1 NA
## 2 6
## 3 4
## 4 5
## 5 4
## 6 NA
## COGSTATE COGDECLN FNCDTCOG COGDXCL EDUCYRS
## 1 Normal Cognition (PD-NC) No No 50% - 89% 18
## 2 Normal Cognition (PD-NC) No No 90% - 100% 16
## 3 Normal Cognition (PD-NC) Yes No 50% - 89% 16
## 4 Normal Cognition (PD-NC) No No 50% - 89% 16
## 5 Normal Cognition (PD-NC) Yes No 50% - 89% 16
## 6 <NA> <NA> <NA> <NA> 14
## RECRUITMENT_CAT APPRDX
## 1 HC Healthy Control
## 2 PD Parkinson's disease
## 3 PD Parkinson's disease
## 4 PD Parkinson's disease
## 5 HC Healthy Control
## 6 PD Parkinson's disease#longitudinal analysis
ppmi_longit<-read.csv("ppmi_top_wide_UPDRS.csv",header=TRUE)
clinical<-read.csv("ppmi_clinical.csv",header=TRUE)
merged_dataset<-merge(ppmi_longit,clinical,by="FID_IID",all.x=TRUE)
ppmi_long<-merged_dataset[,c(1:342,351:354,364,365,347)]
head(ppmi_long)## FID_IID X.1.x X.x L_insular_cortex_AvgMeanCurvature
## 1 3001 1 2 0.110445
## 2 3001 4852 2 0.110445
## 3 3001 2206 2 0.110445
## 4 3001 2647 2 0.110445
## 5 3001 3970 2 0.110445
## 6 3001 1765 2 0.110445
## L_insular_cortex_ComputeArea L_insular_cortex_Volume
## 1 2917.938 8754.107
## 2 2917.938 8754.107
## 3 2917.938 8754.107
## 4 2917.938 8754.107
## 5 2917.938 8754.107
## 6 2917.938 8754.107
## L_insular_cortex_ShapeIndex L_insular_cortex_Curvedness
## 1 0.3207401 0.1033839
## 2 0.3207401 0.1033839
## 3 0.3207401 0.1033839
## 4 0.3207401 0.1033839
## 5 0.3207401 0.1033839
## 6 0.3207401 0.1033839
## R_insular_cortex_AvgMeanCurvature R_insular_cortex_ComputeArea
## 1 0.1160668 2342.961
## 2 0.1160668 2342.961
## 3 0.1160668 2342.961
## 4 0.1160668 2342.961
## 5 0.1160668 2342.961
## 6 0.1160668 2342.961
## R_insular_cortex_Volume R_insular_cortex_ShapeIndex
## 1 5732.367 0.314957
## 2 5732.367 0.314957
## 3 5732.367 0.314957
## 4 5732.367 0.314957
## 5 5732.367 0.314957
## 6 5732.367 0.314957
## R_insular_cortex_Curvedness L_cingulate_gyrus_AvgMeanCurvature
## 1 0.1215921 0.1246343
## 2 0.1215921 0.1246343
## 3 0.1215921 0.1246343
## 4 0.1215921 0.1246343
## 5 0.1215921 0.1246343
## 6 0.1215921 0.1246343
## L_cingulate_gyrus_ComputeArea L_cingulate_gyrus_Volume
## 1 4381.93 11205.13
## 2 4381.93 11205.13
## 3 4381.93 11205.13
## 4 4381.93 11205.13
## 5 4381.93 11205.13
## 6 4381.93 11205.13
## L_cingulate_gyrus_ShapeIndex L_cingulate_gyrus_Curvedness
## 1 0.4349292 0.06714417
## 2 0.4349292 0.06714417
## 3 0.4349292 0.06714417
## 4 0.4349292 0.06714417
## 5 0.4349292 0.06714417
## 6 0.4349292 0.06714417
## R_cingulate_gyrus_AvgMeanCurvature R_cingulate_gyrus_ComputeArea
## 1 0.1249111 4610.447
## 2 0.1249111 4610.447
## 3 0.1249111 4610.447
## 4 0.1249111 4610.447
## 5 0.1249111 4610.447
## 6 0.1249111 4610.447
## R_cingulate_gyrus_Volume R_cingulate_gyrus_ShapeIndex
## 1 12246.55 0.4350192
## 2 12246.55 0.4350192
## 3 12246.55 0.4350192
## 4 12246.55 0.4350192
## 5 12246.55 0.4350192
## 6 12246.55 0.4350192
## R_cingulate_gyrus_Curvedness L_caudate_AvgMeanCurvature
## 1 0.07081687 0.2392929
## 2 0.07081687 0.2392929
## 3 0.07081687 0.2392929
## 4 0.07081687 0.2392929
## 5 0.07081687 0.2392929
## 6 0.07081687 0.2392929
## L_caudate_ComputeArea L_caudate_Volume L_caudate_ShapeIndex
## 1 621.5344 821.8991 0.2473931
## 2 621.5344 821.8991 0.2473931
## 3 621.5344 821.8991 0.2473931
## 4 621.5344 821.8991 0.2473931
## 5 621.5344 821.8991 0.2473931
## 6 621.5344 821.8991 0.2473931
## L_caudate_Curvedness R_caudate_AvgMeanCurvature R_caudate_ComputeArea
## 1 0.342922 0.1558452 1302.146
## 2 0.342922 0.1558452 1302.146
## 3 0.342922 0.1558452 1302.146
## 4 0.342922 0.1558452 1302.146
## 5 0.342922 0.1558452 1302.146
## 6 0.342922 0.1558452 1302.146
## R_caudate_Volume R_caudate_ShapeIndex R_caudate_Curvedness
## 1 2526.248 0.2892424 0.1496714
## 2 2526.248 0.2892424 0.1496714
## 3 2526.248 0.2892424 0.1496714
## 4 2526.248 0.2892424 0.1496714
## 5 2526.248 0.2892424 0.1496714
## 6 2526.248 0.2892424 0.1496714
## L_putamen_AvgMeanCurvature L_putamen_ComputeArea L_putamen_Volume
## 1 0.2022735 1029.175 1543.017
## 2 0.2022735 1029.175 1543.017
## 3 0.2022735 1029.175 1543.017
## 4 0.2022735 1029.175 1543.017
## 5 0.2022735 1029.175 1543.017
## 6 0.2022735 1029.175 1543.017
## L_putamen_ShapeIndex L_putamen_Curvedness R_putamen_AvgMeanCurvature
## 1 0.2853486 0.2320214 0.1111208
## 2 0.2853486 0.2320214 0.1111208
## 3 0.2853486 0.2320214 0.1111208
## 4 0.2853486 0.2320214 0.1111208
## 5 0.2853486 0.2320214 0.1111208
## 6 0.2853486 0.2320214 0.1111208
## R_putamen_ComputeArea R_putamen_Volume R_putamen_ShapeIndex
## 1 1680.197 3792.201 0.2670927
## 2 1680.197 3792.201 0.2670927
## 3 1680.197 3792.201 0.2670927
## 4 1680.197 3792.201 0.2670927
## 5 1680.197 3792.201 0.2670927
## 6 1680.197 3792.201 0.2670927
## R_putamen_Curvedness L_hippocampus_AvgMeanCurvature
## 1 0.1332011 0.1226265
## 2 0.1332011 0.1226265
## 3 0.1332011 0.1226265
## 4 0.1332011 0.1226265
## 5 0.1332011 0.1226265
## 6 0.1332011 0.1226265
## L_hippocampus_ComputeArea L_hippocampus_Volume L_hippocampus_ShapeIndex
## 1 1769.672 4737.038 0.3201427
## 2 1769.672 4737.038 0.3201427
## 3 1769.672 4737.038 0.3201427
## 4 1769.672 4737.038 0.3201427
## 5 1769.672 4737.038 0.3201427
## 6 1769.672 4737.038 0.3201427
## L_hippocampus_Curvedness R_hippocampus_AvgMeanCurvature
## 1 0.1179703 0.1312485
## 2 0.1179703 0.1312485
## 3 0.1179703 0.1312485
## 4 0.1179703 0.1312485
## 5 0.1179703 0.1312485
## 6 0.1179703 0.1312485
## R_hippocampus_ComputeArea R_hippocampus_Volume R_hippocampus_ShapeIndex
## 1 1578.946 3817.621 0.2894075
## 2 1578.946 3817.621 0.2894075
## 3 1578.946 3817.621 0.2894075
## 4 1578.946 3817.621 0.2894075
## 5 1578.946 3817.621 0.2894075
## 6 1578.946 3817.621 0.2894075
## R_hippocampus_Curvedness cerebellum_AvgMeanCurvature
## 1 0.1482493 -0.01081359
## 2 0.1482493 -0.01081359
## 3 0.1482493 -0.01081359
## 4 0.1482493 -0.01081359
## 5 0.1482493 -0.01081359
## 6 0.1482493 -0.01081359
## cerebellum_ComputeArea cerebellum_Volume cerebellum_ShapeIndex
## 1 20909.58 185742.6 0.2932517
## 2 20909.58 185742.6 0.2932517
## 3 20909.58 185742.6 0.2932517
## 4 20909.58 185742.6 0.2932517
## 5 20909.58 185742.6 0.2932517
## 6 20909.58 185742.6 0.2932517
## cerebellum_Curvedness brainstem_AvgMeanCurvature brainstem_ComputeArea
## 1 0.1674615 0.06346277 7741.742
## 2 0.1674615 0.06346277 7741.742
## 3 0.1674615 0.06346277 7741.742
## 4 0.1674615 0.06346277 7741.742
## 5 0.1674615 0.06346277 7741.742
## 6 0.1674615 0.06346277 7741.742
## brainstem_Volume brainstem_ShapeIndex brainstem_Curvedness
## 1 45610.19 0.3192699 0.07574226
## 2 45610.19 0.3192699 0.07574226
## 3 45610.19 0.3192699 0.07574226
## 4 45610.19 0.3192699 0.07574226
## 5 45610.19 0.3192699 0.07574226
## 6 45610.19 0.3192699 0.07574226
## L_superior_frontal_gyrus_AvgMeanCurvature
## 1 0.07951369
## 2 0.07951369
## 3 0.07951369
## 4 0.07951369
## 5 0.07951369
## 6 0.07951369
## L_superior_frontal_gyrus_ComputeArea L_superior_frontal_gyrus_Volume
## 1 11363.7 53308.55
## 2 11363.7 53308.55
## 3 11363.7 53308.55
## 4 11363.7 53308.55
## 5 11363.7 53308.55
## 6 11363.7 53308.55
## L_superior_frontal_gyrus_ShapeIndex L_superior_frontal_gyrus_Curvedness
## 1 0.3744766 0.05133303
## 2 0.3744766 0.05133303
## 3 0.3744766 0.05133303
## 4 0.3744766 0.05133303
## 5 0.3744766 0.05133303
## 6 0.3744766 0.05133303
## R_superior_frontal_gyrus_AvgMeanCurvature
## 1 0.08691916
## 2 0.08691916
## 3 0.08691916
## 4 0.08691916
## 5 0.08691916
## 6 0.08691916
## R_superior_frontal_gyrus_ComputeArea R_superior_frontal_gyrus_Volume
## 1 11134.17 52396.24
## 2 11134.17 52396.24
## 3 11134.17 52396.24
## 4 11134.17 52396.24
## 5 11134.17 52396.24
## 6 11134.17 52396.24
## R_superior_frontal_gyrus_ShapeIndex R_superior_frontal_gyrus_Curvedness
## 1 0.4484875 0.04586595
## 2 0.4484875 0.04586595
## 3 0.4484875 0.04586595
## 4 0.4484875 0.04586595
## 5 0.4484875 0.04586595
## 6 0.4484875 0.04586595
## L_middle_frontal_gyrus_AvgMeanCurvature
## 1 0.1195262
## 2 0.1195262
## 3 0.1195262
## 4 0.1195262
## 5 0.1195262
## 6 0.1195262
## L_middle_frontal_gyrus_ComputeArea L_middle_frontal_gyrus_Volume
## 1 9988.897 52452.81
## 2 9988.897 52452.81
## 3 9988.897 52452.81
## 4 9988.897 52452.81
## 5 9988.897 52452.81
## 6 9988.897 52452.81
## L_middle_frontal_gyrus_ShapeIndex L_middle_frontal_gyrus_Curvedness
## 1 0.3481521 0.06595413
## 2 0.3481521 0.06595413
## 3 0.3481521 0.06595413
## 4 0.3481521 0.06595413
## 5 0.3481521 0.06595413
## 6 0.3481521 0.06595413
## R_middle_frontal_gyrus_AvgMeanCurvature
## 1 0.07785072
## 2 0.07785072
## 3 0.07785072
## 4 0.07785072
## 5 0.07785072
## 6 0.07785072
## R_middle_frontal_gyrus_ComputeArea R_middle_frontal_gyrus_Volume
## 1 9660.306 54012.12
## 2 9660.306 54012.12
## 3 9660.306 54012.12
## 4 9660.306 54012.12
## 5 9660.306 54012.12
## 6 9660.306 54012.12
## R_middle_frontal_gyrus_ShapeIndex R_middle_frontal_gyrus_Curvedness
## 1 0.3178475 0.05932451
## 2 0.3178475 0.05932451
## 3 0.3178475 0.05932451
## 4 0.3178475 0.05932451
## 5 0.3178475 0.05932451
## 6 0.3178475 0.05932451
## L_inferior_frontal_gyrus_AvgMeanCurvature
## 1 0.08264828
## 2 0.08264828
## 3 0.08264828
## 4 0.08264828
## 5 0.08264828
## 6 0.08264828
## L_inferior_frontal_gyrus_ComputeArea L_inferior_frontal_gyrus_Volume
## 1 5091.727 25113.25
## 2 5091.727 25113.25
## 3 5091.727 25113.25
## 4 5091.727 25113.25
## 5 5091.727 25113.25
## 6 5091.727 25113.25
## L_inferior_frontal_gyrus_ShapeIndex L_inferior_frontal_gyrus_Curvedness
## 1 0.3067739 0.09213249
## 2 0.3067739 0.09213249
## 3 0.3067739 0.09213249
## 4 0.3067739 0.09213249
## 5 0.3067739 0.09213249
## 6 0.3067739 0.09213249
## R_inferior_frontal_gyrus_AvgMeanCurvature
## 1 0.07610716
## 2 0.07610716
## 3 0.07610716
## 4 0.07610716
## 5 0.07610716
## 6 0.07610716
## R_inferior_frontal_gyrus_ComputeArea R_inferior_frontal_gyrus_Volume
## 1 5612.835 28546.87
## 2 5612.835 28546.87
## 3 5612.835 28546.87
## 4 5612.835 28546.87
## 5 5612.835 28546.87
## 6 5612.835 28546.87
## R_inferior_frontal_gyrus_ShapeIndex R_inferior_frontal_gyrus_Curvedness
## 1 0.3206594 0.06914873
## 2 0.3206594 0.06914873
## 3 0.3206594 0.06914873
## 4 0.3206594 0.06914873
## 5 0.3206594 0.06914873
## 6 0.3206594 0.06914873
## L_precentral_gyrus_AvgMeanCurvature L_precentral_gyrus_ComputeArea
## 1 0.09409574 6634.533
## 2 0.09409574 6634.533
## 3 0.09409574 6634.533
## 4 0.09409574 6634.533
## 5 0.09409574 6634.533
## 6 0.09409574 6634.533
## L_precentral_gyrus_Volume L_precentral_gyrus_ShapeIndex
## 1 25205.3 0.351831
## 2 25205.3 0.351831
## 3 25205.3 0.351831
## 4 25205.3 0.351831
## 5 25205.3 0.351831
## 6 25205.3 0.351831
## L_precentral_gyrus_Curvedness R_precentral_gyrus_AvgMeanCurvature
## 1 0.09609143 0.08857492
## 2 0.09609143 0.08857492
## 3 0.09609143 0.08857492
## 4 0.09609143 0.08857492
## 5 0.09609143 0.08857492
## 6 0.09609143 0.08857492
## R_precentral_gyrus_ComputeArea R_precentral_gyrus_Volume
## 1 6057.209 23691.55
## 2 6057.209 23691.55
## 3 6057.209 23691.55
## 4 6057.209 23691.55
## 5 6057.209 23691.55
## 6 6057.209 23691.55
## R_precentral_gyrus_ShapeIndex R_precentral_gyrus_Curvedness
## 1 0.3654834 0.07582953
## 2 0.3654834 0.07582953
## 3 0.3654834 0.07582953
## 4 0.3654834 0.07582953
## 5 0.3654834 0.07582953
## 6 0.3654834 0.07582953
## L_middle_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.09504461
## 2 0.09504461
## 3 0.09504461
## 4 0.09504461
## 5 0.09504461
## 6 0.09504461
## L_middle_orbitofrontal_gyrus_ComputeArea
## 1 3190.647
## 2 3190.647
## 3 3190.647
## 4 3190.647
## 5 3190.647
## 6 3190.647
## L_middle_orbitofrontal_gyrus_Volume
## 1 10459.92
## 2 10459.92
## 3 10459.92
## 4 10459.92
## 5 10459.92
## 6 10459.92
## L_middle_orbitofrontal_gyrus_ShapeIndex
## 1 0.2909523
## 2 0.2909523
## 3 0.2909523
## 4 0.2909523
## 5 0.2909523
## 6 0.2909523
## L_middle_orbitofrontal_gyrus_Curvedness
## 1 0.08821871
## 2 0.08821871
## 3 0.08821871
## 4 0.08821871
## 5 0.08821871
## 6 0.08821871
## R_middle_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1093085
## 2 0.1093085
## 3 0.1093085
## 4 0.1093085
## 5 0.1093085
## 6 0.1093085
## R_middle_orbitofrontal_gyrus_ComputeArea
## 1 3108.103
## 2 3108.103
## 3 3108.103
## 4 3108.103
## 5 3108.103
## 6 3108.103
## R_middle_orbitofrontal_gyrus_Volume
## 1 9611.13
## 2 9611.13
## 3 9611.13
## 4 9611.13
## 5 9611.13
## 6 9611.13
## R_middle_orbitofrontal_gyrus_ShapeIndex
## 1 0.306004
## 2 0.306004
## 3 0.306004
## 4 0.306004
## 5 0.306004
## 6 0.306004
## R_middle_orbitofrontal_gyrus_Curvedness
## 1 0.09612964
## 2 0.09612964
## 3 0.09612964
## 4 0.09612964
## 5 0.09612964
## 6 0.09612964
## L_lateral_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1274801
## 2 0.1274801
## 3 0.1274801
## 4 0.1274801
## 5 0.1274801
## 6 0.1274801
## L_lateral_orbitofrontal_gyrus_ComputeArea
## 1 1940.454
## 2 1940.454
## 3 1940.454
## 4 1940.454
## 5 1940.454
## 6 1940.454
## L_lateral_orbitofrontal_gyrus_Volume
## 1 4320.333
## 2 4320.333
## 3 4320.333
## 4 4320.333
## 5 4320.333
## 6 4320.333
## L_lateral_orbitofrontal_gyrus_ShapeIndex
## 1 0.2895921
## 2 0.2895921
## 3 0.2895921
## 4 0.2895921
## 5 0.2895921
## 6 0.2895921
## L_lateral_orbitofrontal_gyrus_Curvedness
## 1 0.1775847
## 2 0.1775847
## 3 0.1775847
## 4 0.1775847
## 5 0.1775847
## 6 0.1775847
## R_lateral_orbitofrontal_gyrus_AvgMeanCurvature
## 1 0.1397253
## 2 0.1397253
## 3 0.1397253
## 4 0.1397253
## 5 0.1397253
## 6 0.1397253
## R_lateral_orbitofrontal_gyrus_ComputeArea
## 1 1953.675
## 2 1953.675
## 3 1953.675
## 4 1953.675
## 5 1953.675
## 6 1953.675
## R_lateral_orbitofrontal_gyrus_Volume
## 1 4093.492
## 2 4093.492
## 3 4093.492
## 4 4093.492
## 5 4093.492
## 6 4093.492
## R_lateral_orbitofrontal_gyrus_ShapeIndex
## 1 0.2971379
## 2 0.2971379
## 3 0.2971379
## 4 0.2971379
## 5 0.2971379
## 6 0.2971379
## R_lateral_orbitofrontal_gyrus_Curvedness L_gyrus_rectus_AvgMeanCurvature
## 1 0.1634671 0.2078852
## 2 0.1634671 0.2078852
## 3 0.1634671 0.2078852
## 4 0.1634671 0.2078852
## 5 0.1634671 0.2078852
## 6 0.1634671 0.2078852
## L_gyrus_rectus_ComputeArea L_gyrus_rectus_Volume
## 1 1143.078 1785.822
## 2 1143.078 1785.822
## 3 1143.078 1785.822
## 4 1143.078 1785.822
## 5 1143.078 1785.822
## 6 1143.078 1785.822
## L_gyrus_rectus_ShapeIndex L_gyrus_rectus_Curvedness
## 1 0.2893511 0.2222131
## 2 0.2893511 0.2222131
## 3 0.2893511 0.2222131
## 4 0.2893511 0.2222131
## 5 0.2893511 0.2222131
## 6 0.2893511 0.2222131
## R_gyrus_rectus_AvgMeanCurvature R_gyrus_rectus_ComputeArea
## 1 0.1519435 1408.899
## 2 0.1519435 1408.899
## 3 0.1519435 1408.899
## 4 0.1519435 1408.899
## 5 0.1519435 1408.899
## 6 0.1519435 1408.899
## R_gyrus_rectus_Volume R_gyrus_rectus_ShapeIndex
## 1 2763.942 0.261203
## 2 2763.942 0.261203
## 3 2763.942 0.261203
## 4 2763.942 0.261203
## 5 2763.942 0.261203
## 6 2763.942 0.261203
## R_gyrus_rectus_Curvedness L_postcentral_gyrus_AvgMeanCurvature
## 1 0.1959469 0.09087086
## 2 0.1959469 0.09087086
## 3 0.1959469 0.09087086
## 4 0.1959469 0.09087086
## 5 0.1959469 0.09087086
## 6 0.1959469 0.09087086
## L_postcentral_gyrus_ComputeArea L_postcentral_gyrus_Volume
## 1 5952.575 22426.55
## 2 5952.575 22426.55
## 3 5952.575 22426.55
## 4 5952.575 22426.55
## 5 5952.575 22426.55
## 6 5952.575 22426.55
## L_postcentral_gyrus_ShapeIndex L_postcentral_gyrus_Curvedness
## 1 0.3305127 0.08240747
## 2 0.3305127 0.08240747
## 3 0.3305127 0.08240747
## 4 0.3305127 0.08240747
## 5 0.3305127 0.08240747
## 6 0.3305127 0.08240747
## R_postcentral_gyrus_AvgMeanCurvature R_postcentral_gyrus_ComputeArea
## 1 0.07717761 6274.401
## 2 0.07717761 6274.401
## 3 0.07717761 6274.401
## 4 0.07717761 6274.401
## 5 0.07717761 6274.401
## 6 0.07717761 6274.401
## R_postcentral_gyrus_Volume R_postcentral_gyrus_ShapeIndex
## 1 26116.1 0.3320086
## 2 26116.1 0.3320086
## 3 26116.1 0.3320086
## 4 26116.1 0.3320086
## 5 26116.1 0.3320086
## 6 26116.1 0.3320086
## R_postcentral_gyrus_Curvedness
## 1 0.08080232
## 2 0.08080232
## 3 0.08080232
## 4 0.08080232
## 5 0.08080232
## 6 0.08080232
## L_superior_parietal_gyrus_AvgMeanCurvature
## 1 0.156426
## 2 0.156426
## 3 0.156426
## 4 0.156426
## 5 0.156426
## 6 0.156426
## L_superior_parietal_gyrus_ComputeArea L_superior_parietal_gyrus_Volume
## 1 5228.112 22835.86
## 2 5228.112 22835.86
## 3 5228.112 22835.86
## 4 5228.112 22835.86
## 5 5228.112 22835.86
## 6 5228.112 22835.86
## L_superior_parietal_gyrus_ShapeIndex
## 1 0.3180919
## 2 0.3180919
## 3 0.3180919
## 4 0.3180919
## 5 0.3180919
## 6 0.3180919
## L_superior_parietal_gyrus_Curvedness
## 1 0.08668642
## 2 0.08668642
## 3 0.08668642
## 4 0.08668642
## 5 0.08668642
## 6 0.08668642
## R_superior_parietal_gyrus_AvgMeanCurvature
## 1 0.0780768
## 2 0.0780768
## 3 0.0780768
## 4 0.0780768
## 5 0.0780768
## 6 0.0780768
## R_superior_parietal_gyrus_ComputeArea R_superior_parietal_gyrus_Volume
## 1 5121.035 25387.02
## 2 5121.035 25387.02
## 3 5121.035 25387.02
## 4 5121.035 25387.02
## 5 5121.035 25387.02
## 6 5121.035 25387.02
## R_superior_parietal_gyrus_ShapeIndex
## 1 0.2799022
## 2 0.2799022
## 3 0.2799022
## 4 0.2799022
## 5 0.2799022
## 6 0.2799022
## R_superior_parietal_gyrus_Curvedness
## 1 0.08711799
## 2 0.08711799
## 3 0.08711799
## 4 0.08711799
## 5 0.08711799
## 6 0.08711799
## L_supramarginal_gyrus_AvgMeanCurvature L_supramarginal_gyrus_ComputeArea
## 1 0.08960319 3766.59
## 2 0.08960319 3766.59
## 3 0.08960319 3766.59
## 4 0.08960319 3766.59
## 5 0.08960319 3766.59
## 6 0.08960319 3766.59
## L_supramarginal_gyrus_Volume L_supramarginal_gyrus_ShapeIndex
## 1 14904.22 0.3068543
## 2 14904.22 0.3068543
## 3 14904.22 0.3068543
## 4 14904.22 0.3068543
## 5 14904.22 0.3068543
## 6 14904.22 0.3068543
## L_supramarginal_gyrus_Curvedness R_supramarginal_gyrus_AvgMeanCurvature
## 1 0.09030263 0.09359056
## 2 0.09030263 0.09359056
## 3 0.09030263 0.09359056
## 4 0.09030263 0.09359056
## 5 0.09030263 0.09359056
## 6 0.09030263 0.09359056
## R_supramarginal_gyrus_ComputeArea R_supramarginal_gyrus_Volume
## 1 2943.982 11236.22
## 2 2943.982 11236.22
## 3 2943.982 11236.22
## 4 2943.982 11236.22
## 5 2943.982 11236.22
## 6 2943.982 11236.22
## R_supramarginal_gyrus_ShapeIndex R_supramarginal_gyrus_Curvedness
## 1 0.3043556 0.113241
## 2 0.3043556 0.113241
## 3 0.3043556 0.113241
## 4 0.3043556 0.113241
## 5 0.3043556 0.113241
## 6 0.3043556 0.113241
## L_angular_gyrus_AvgMeanCurvature L_angular_gyrus_ComputeArea
## 1 0.08332833 4499.945
## 2 0.08332833 4499.945
## 3 0.08332833 4499.945
## 4 0.08332833 4499.945
## 5 0.08332833 4499.945
## 6 0.08332833 4499.945
## L_angular_gyrus_Volume L_angular_gyrus_ShapeIndex
## 1 18707.85 0.3069968
## 2 18707.85 0.3069968
## 3 18707.85 0.3069968
## 4 18707.85 0.3069968
## 5 18707.85 0.3069968
## 6 18707.85 0.3069968
## L_angular_gyrus_Curvedness R_angular_gyrus_AvgMeanCurvature
## 1 0.08690669 0.1024264
## 2 0.08690669 0.1024264
## 3 0.08690669 0.1024264
## 4 0.08690669 0.1024264
## 5 0.08690669 0.1024264
## 6 0.08690669 0.1024264
## R_angular_gyrus_ComputeArea R_angular_gyrus_Volume
## 1 3666.847 13003.38
## 2 3666.847 13003.38
## 3 3666.847 13003.38
## 4 3666.847 13003.38
## 5 3666.847 13003.38
## 6 3666.847 13003.38
## R_angular_gyrus_ShapeIndex R_angular_gyrus_Curvedness
## 1 0.2994884 0.1106102
## 2 0.2994884 0.1106102
## 3 0.2994884 0.1106102
## 4 0.2994884 0.1106102
## 5 0.2994884 0.1106102
## 6 0.2994884 0.1106102
## L_precuneus_AvgMeanCurvature L_precuneus_ComputeArea L_precuneus_Volume
## 1 0.1026454 3065.234 9302.229
## 2 0.1026454 3065.234 9302.229
## 3 0.1026454 3065.234 9302.229
## 4 0.1026454 3065.234 9302.229
## 5 0.1026454 3065.234 9302.229
## 6 0.1026454 3065.234 9302.229
## L_precuneus_ShapeIndex L_precuneus_Curvedness
## 1 0.3086211 0.0969586
## 2 0.3086211 0.0969586
## 3 0.3086211 0.0969586
## 4 0.3086211 0.0969586
## 5 0.3086211 0.0969586
## 6 0.3086211 0.0969586
## R_precuneus_AvgMeanCurvature R_precuneus_ComputeArea R_precuneus_Volume
## 1 0.1078925 3332.431 11293.9
## 2 0.1078925 3332.431 11293.9
## 3 0.1078925 3332.431 11293.9
## 4 0.1078925 3332.431 11293.9
## 5 0.1078925 3332.431 11293.9
## 6 0.1078925 3332.431 11293.9
## R_precuneus_ShapeIndex R_precuneus_Curvedness
## 1 0.3032337 0.09921845
## 2 0.3032337 0.09921845
## 3 0.3032337 0.09921845
## 4 0.3032337 0.09921845
## 5 0.3032337 0.09921845
## 6 0.3032337 0.09921845
## L_superior_occipital_gyrus_AvgMeanCurvature
## 1 0.120578
## 2 0.120578
## 3 0.120578
## 4 0.120578
## 5 0.120578
## 6 0.120578
## L_superior_occipital_gyrus_ComputeArea L_superior_occipital_gyrus_Volume
## 1 1909.412 5150.974
## 2 1909.412 5150.974
## 3 1909.412 5150.974
## 4 1909.412 5150.974
## 5 1909.412 5150.974
## 6 1909.412 5150.974
## L_superior_occipital_gyrus_ShapeIndex
## 1 0.271993
## 2 0.271993
## 3 0.271993
## 4 0.271993
## 5 0.271993
## 6 0.271993
## L_superior_occipital_gyrus_Curvedness
## 1 0.1500819
## 2 0.1500819
## 3 0.1500819
## 4 0.1500819
## 5 0.1500819
## 6 0.1500819
## R_superior_occipital_gyrus_AvgMeanCurvature
## 1 0.1171069
## 2 0.1171069
## 3 0.1171069
## 4 0.1171069
## 5 0.1171069
## 6 0.1171069
## R_superior_occipital_gyrus_ComputeArea R_superior_occipital_gyrus_Volume
## 1 2173.619 6231.256
## 2 2173.619 6231.256
## 3 2173.619 6231.256
## 4 2173.619 6231.256
## 5 2173.619 6231.256
## 6 2173.619 6231.256
## R_superior_occipital_gyrus_ShapeIndex
## 1 0.2585772
## 2 0.2585772
## 3 0.2585772
## 4 0.2585772
## 5 0.2585772
## 6 0.2585772
## R_superior_occipital_gyrus_Curvedness
## 1 0.1520477
## 2 0.1520477
## 3 0.1520477
## 4 0.1520477
## 5 0.1520477
## 6 0.1520477
## L_middle_occipital_gyrus_AvgMeanCurvature
## 1 0.08442675
## 2 0.08442675
## 3 0.08442675
## 4 0.08442675
## 5 0.08442675
## 6 0.08442675
## L_middle_occipital_gyrus_ComputeArea L_middle_occipital_gyrus_Volume
## 1 4503.611 17128.71
## 2 4503.611 17128.71
## 3 4503.611 17128.71
## 4 4503.611 17128.71
## 5 4503.611 17128.71
## 6 4503.611 17128.71
## L_middle_occipital_gyrus_ShapeIndex L_middle_occipital_gyrus_Curvedness
## 1 0.3417904 0.08082197
## 2 0.3417904 0.08082197
## 3 0.3417904 0.08082197
## 4 0.3417904 0.08082197
## 5 0.3417904 0.08082197
## 6 0.3417904 0.08082197
## R_middle_occipital_gyrus_AvgMeanCurvature
## 1 0.09667509
## 2 0.09667509
## 3 0.09667509
## 4 0.09667509
## 5 0.09667509
## 6 0.09667509
## R_middle_occipital_gyrus_ComputeArea R_middle_occipital_gyrus_Volume
## 1 5311.343 20127.27
## 2 5311.343 20127.27
## 3 5311.343 20127.27
## 4 5311.343 20127.27
## 5 5311.343 20127.27
## 6 5311.343 20127.27
## R_middle_occipital_gyrus_ShapeIndex R_middle_occipital_gyrus_Curvedness
## 1 0.3181087 0.08639906
## 2 0.3181087 0.08639906
## 3 0.3181087 0.08639906
## 4 0.3181087 0.08639906
## 5 0.3181087 0.08639906
## 6 0.3181087 0.08639906
## L_inferior_occipital_gyrus_AvgMeanCurvature
## 1 0.1102742
## 2 0.1102742
## 3 0.1102742
## 4 0.1102742
## 5 0.1102742
## 6 0.1102742
## L_inferior_occipital_gyrus_ComputeArea L_inferior_occipital_gyrus_Volume
## 1 2575.385 7956.538
## 2 2575.385 7956.538
## 3 2575.385 7956.538
## 4 2575.385 7956.538
## 5 2575.385 7956.538
## 6 2575.385 7956.538
## L_inferior_occipital_gyrus_ShapeIndex
## 1 0.3179899
## 2 0.3179899
## 3 0.3179899
## 4 0.3179899
## 5 0.3179899
## 6 0.3179899
## L_inferior_occipital_gyrus_Curvedness
## 1 0.1061467
## 2 0.1061467
## 3 0.1061467
## 4 0.1061467
## 5 0.1061467
## 6 0.1061467
## R_inferior_occipital_gyrus_AvgMeanCurvature
## 1 0.08905351
## 2 0.08905351
## 3 0.08905351
## 4 0.08905351
## 5 0.08905351
## 6 0.08905351
## R_inferior_occipital_gyrus_ComputeArea R_inferior_occipital_gyrus_Volume
## 1 3322.39 12708.51
## 2 3322.39 12708.51
## 3 3322.39 12708.51
## 4 3322.39 12708.51
## 5 3322.39 12708.51
## 6 3322.39 12708.51
## R_inferior_occipital_gyrus_ShapeIndex
## 1 0.3491609
## 2 0.3491609
## 3 0.3491609
## 4 0.3491609
## 5 0.3491609
## 6 0.3491609
## R_inferior_occipital_gyrus_Curvedness L_cuneus_AvgMeanCurvature
## 1 0.0768138 0.1215609
## 2 0.0768138 0.1215609
## 3 0.0768138 0.1215609
## 4 0.0768138 0.1215609
## 5 0.0768138 0.1215609
## 6 0.0768138 0.1215609
## L_cuneus_ComputeArea L_cuneus_Volume L_cuneus_ShapeIndex
## 1 2430.494 6097.885 0.300488
## 2 2430.494 6097.885 0.300488
## 3 2430.494 6097.885 0.300488
## 4 2430.494 6097.885 0.300488
## 5 2430.494 6097.885 0.300488
## 6 2430.494 6097.885 0.300488
## L_cuneus_Curvedness R_cuneus_AvgMeanCurvature R_cuneus_ComputeArea
## 1 0.1215284 0.1052953 2722.789
## 2 0.1215284 0.1052953 2722.789
## 3 0.1215284 0.1052953 2722.789
## 4 0.1215284 0.1052953 2722.789
## 5 0.1215284 0.1052953 2722.789
## 6 0.1215284 0.1052953 2722.789
## R_cuneus_Volume R_cuneus_ShapeIndex R_cuneus_Curvedness
## 1 8342.228 0.2938111 0.1017114
## 2 8342.228 0.2938111 0.1017114
## 3 8342.228 0.2938111 0.1017114
## 4 8342.228 0.2938111 0.1017114
## 5 8342.228 0.2938111 0.1017114
## 6 8342.228 0.2938111 0.1017114
## L_superior_temporal_gyrus_AvgMeanCurvature
## 1 0.08818216
## 2 0.08818216
## 3 0.08818216
## 4 0.08818216
## 5 0.08818216
## 6 0.08818216
## L_superior_temporal_gyrus_ComputeArea L_superior_temporal_gyrus_Volume
## 1 7677.646 32329.76
## 2 7677.646 32329.76
## 3 7677.646 32329.76
## 4 7677.646 32329.76
## 5 7677.646 32329.76
## 6 7677.646 32329.76
## L_superior_temporal_gyrus_ShapeIndex
## 1 0.3832396
## 2 0.3832396
## 3 0.3832396
## 4 0.3832396
## 5 0.3832396
## 6 0.3832396
## L_superior_temporal_gyrus_Curvedness
## 1 0.06207907
## 2 0.06207907
## 3 0.06207907
## 4 0.06207907
## 5 0.06207907
## 6 0.06207907
## R_superior_temporal_gyrus_AvgMeanCurvature
## 1 0.08121426
## 2 0.08121426
## 3 0.08121426
## 4 0.08121426
## 5 0.08121426
## 6 0.08121426
## R_superior_temporal_gyrus_ComputeArea R_superior_temporal_gyrus_Volume
## 1 6343.222 26888.9
## 2 6343.222 26888.9
## 3 6343.222 26888.9
## 4 6343.222 26888.9
## 5 6343.222 26888.9
## 6 6343.222 26888.9
## R_superior_temporal_gyrus_ShapeIndex
## 1 0.3733774
## 2 0.3733774
## 3 0.3733774
## 4 0.3733774
## 5 0.3733774
## 6 0.3733774
## R_superior_temporal_gyrus_Curvedness
## 1 0.06523898
## 2 0.06523898
## 3 0.06523898
## 4 0.06523898
## 5 0.06523898
## 6 0.06523898
## L_middle_temporal_gyrus_AvgMeanCurvature
## 1 0.1057315
## 2 0.1057315
## 3 0.1057315
## 4 0.1057315
## 5 0.1057315
## 6 0.1057315
## L_middle_temporal_gyrus_ComputeArea L_middle_temporal_gyrus_Volume
## 1 5470.255 17208.84
## 2 5470.255 17208.84
## 3 5470.255 17208.84
## 4 5470.255 17208.84
## 5 5470.255 17208.84
## 6 5470.255 17208.84
## L_middle_temporal_gyrus_ShapeIndex L_middle_temporal_gyrus_Curvedness
## 1 0.3291948 0.08871739
## 2 0.3291948 0.08871739
## 3 0.3291948 0.08871739
## 4 0.3291948 0.08871739
## 5 0.3291948 0.08871739
## 6 0.3291948 0.08871739
## R_middle_temporal_gyrus_AvgMeanCurvature
## 1 0.09784834
## 2 0.09784834
## 3 0.09784834
## 4 0.09784834
## 5 0.09784834
## 6 0.09784834
## R_middle_temporal_gyrus_ComputeArea R_middle_temporal_gyrus_Volume
## 1 6960.965 27476.33
## 2 6960.965 27476.33
## 3 6960.965 27476.33
## 4 6960.965 27476.33
## 5 6960.965 27476.33
## 6 6960.965 27476.33
## R_middle_temporal_gyrus_ShapeIndex R_middle_temporal_gyrus_Curvedness
## 1 0.3180938 0.07262339
## 2 0.3180938 0.07262339
## 3 0.3180938 0.07262339
## 4 0.3180938 0.07262339
## 5 0.3180938 0.07262339
## 6 0.3180938 0.07262339
## L_inferior_temporal_gyrus_AvgMeanCurvature
## 1 0.08440291
## 2 0.08440291
## 3 0.08440291
## 4 0.08440291
## 5 0.08440291
## 6 0.08440291
## L_inferior_temporal_gyrus_ComputeArea L_inferior_temporal_gyrus_Volume
## 1 5925.988 24980.9
## 2 5925.988 24980.9
## 3 5925.988 24980.9
## 4 5925.988 24980.9
## 5 5925.988 24980.9
## 6 5925.988 24980.9
## L_inferior_temporal_gyrus_ShapeIndex
## 1 0.38634
## 2 0.38634
## 3 0.38634
## 4 0.38634
## 5 0.38634
## 6 0.38634
## L_inferior_temporal_gyrus_Curvedness
## 1 0.06998834
## 2 0.06998834
## 3 0.06998834
## 4 0.06998834
## 5 0.06998834
## 6 0.06998834
## R_inferior_temporal_gyrus_AvgMeanCurvature
## 1 0.1430986
## 2 0.1430986
## 3 0.1430986
## 4 0.1430986
## 5 0.1430986
## 6 0.1430986
## R_inferior_temporal_gyrus_ComputeArea R_inferior_temporal_gyrus_Volume
## 1 5113.919 18076.97
## 2 5113.919 18076.97
## 3 5113.919 18076.97
## 4 5113.919 18076.97
## 5 5113.919 18076.97
## 6 5113.919 18076.97
## R_inferior_temporal_gyrus_ShapeIndex
## 1 0.3730978
## 2 0.3730978
## 3 0.3730978
## 4 0.3730978
## 5 0.3730978
## 6 0.3730978
## R_inferior_temporal_gyrus_Curvedness
## 1 0.09922728
## 2 0.09922728
## 3 0.09922728
## 4 0.09922728
## 5 0.09922728
## 6 0.09922728
## L_parahippocampal_gyrus_AvgMeanCurvature
## 1 0.1077122
## 2 0.1077122
## 3 0.1077122
## 4 0.1077122
## 5 0.1077122
## 6 0.1077122
## L_parahippocampal_gyrus_ComputeArea L_parahippocampal_gyrus_Volume
## 1 2844.754 7480.951
## 2 2844.754 7480.951
## 3 2844.754 7480.951
## 4 2844.754 7480.951
## 5 2844.754 7480.951
## 6 2844.754 7480.951
## L_parahippocampal_gyrus_ShapeIndex L_parahippocampal_gyrus_Curvedness
## 1 0.2607401 0.1022136
## 2 0.2607401 0.1022136
## 3 0.2607401 0.1022136
## 4 0.2607401 0.1022136
## 5 0.2607401 0.1022136
## 6 0.2607401 0.1022136
## R_parahippocampal_gyrus_AvgMeanCurvature
## 1 0.1026529
## 2 0.1026529
## 3 0.1026529
## 4 0.1026529
## 5 0.1026529
## 6 0.1026529
## R_parahippocampal_gyrus_ComputeArea R_parahippocampal_gyrus_Volume
## 1 3198.464 9512.674
## 2 3198.464 9512.674
## 3 3198.464 9512.674
## 4 3198.464 9512.674
## 5 3198.464 9512.674
## 6 3198.464 9512.674
## R_parahippocampal_gyrus_ShapeIndex R_parahippocampal_gyrus_Curvedness
## 1 0.3130394 0.09406152
## 2 0.3130394 0.09406152
## 3 0.3130394 0.09406152
## 4 0.3130394 0.09406152
## 5 0.3130394 0.09406152
## 6 0.3130394 0.09406152
## L_lingual_gyrus_AvgMeanCurvature L_lingual_gyrus_ComputeArea
## 1 0.09506937 3990.686
## 2 0.09506937 3990.686
## 3 0.09506937 3990.686
## 4 0.09506937 3990.686
## 5 0.09506937 3990.686
## 6 0.09506937 3990.686
## L_lingual_gyrus_Volume L_lingual_gyrus_ShapeIndex
## 1 15329.18 0.3268375
## 2 15329.18 0.3268375
## 3 15329.18 0.3268375
## 4 15329.18 0.3268375
## 5 15329.18 0.3268375
## 6 15329.18 0.3268375
## L_lingual_gyrus_Curvedness R_lingual_gyrus_AvgMeanCurvature
## 1 0.08228135 0.08600172
## 2 0.08228135 0.08600172
## 3 0.08228135 0.08600172
## 4 0.08228135 0.08600172
## 5 0.08228135 0.08600172
## 6 0.08228135 0.08600172
## R_lingual_gyrus_ComputeArea R_lingual_gyrus_Volume
## 1 4208.788 16959.6
## 2 4208.788 16959.6
## 3 4208.788 16959.6
## 4 4208.788 16959.6
## 5 4208.788 16959.6
## 6 4208.788 16959.6
## R_lingual_gyrus_ShapeIndex R_lingual_gyrus_Curvedness
## 1 0.320331 0.07572543
## 2 0.320331 0.07572543
## 3 0.320331 0.07572543
## 4 0.320331 0.07572543
## 5 0.320331 0.07572543
## 6 0.320331 0.07572543
## L_fusiform_gyrus_AvgMeanCurvature L_fusiform_gyrus_ComputeArea
## 1 0.1038532 4534.707
## 2 0.1038532 4534.707
## 3 0.1038532 4534.707
## 4 0.1038532 4534.707
## 5 0.1038532 4534.707
## 6 0.1038532 4534.707
## L_fusiform_gyrus_Volume L_fusiform_gyrus_ShapeIndex
## 1 15830.32 0.3102792
## 2 15830.32 0.3102792
## 3 15830.32 0.3102792
## 4 15830.32 0.3102792
## 5 15830.32 0.3102792
## 6 15830.32 0.3102792
## L_fusiform_gyrus_Curvedness R_fusiform_gyrus_AvgMeanCurvature
## 1 0.08341972 0.1063175
## 2 0.08341972 0.1063175
## 3 0.08341972 0.1063175
## 4 0.08341972 0.1063175
## 5 0.08341972 0.1063175
## 6 0.08341972 0.1063175
## R_fusiform_gyrus_ComputeArea R_fusiform_gyrus_Volume
## 1 3945.037 14471.84
## 2 3945.037 14471.84
## 3 3945.037 14471.84
## 4 3945.037 14471.84
## 5 3945.037 14471.84
## 6 3945.037 14471.84
## R_fusiform_gyrus_ShapeIndex R_fusiform_gyrus_Curvedness Sex Weight
## 1 0.3592094 0.0776228 1 74.2
## 2 0.3592094 0.0776228 1 74.2
## 3 0.3592094 0.0776228 1 74.2
## 4 0.3592094 0.0776228 1 74.2
## 5 0.3592094 0.0776228 1 74.2
## 6 0.3592094 0.0776228 1 74.2
## ResearchGroup VisitID Age chr12_rs34637584_GT chr12_rs34637584_DP
## 1 PD 1 65.1808 0 23
## 2 PD 1 65.1808 0 23
## 3 PD 1 65.1808 0 23
## 4 PD 1 65.1808 0 23
## 5 PD 1 65.1808 0 23
## 6 PD 1 65.1808 0 23
## chr12_rs34637584_GQ chr17_rs11868035_GT chr17_rs11868035_DP
## 1 69 1 53
## 2 69 1 53
## 3 69 1 53
## 4 69 1 53
## 5 69 1 53
## 6 69 1 53
## chr17_rs11868035_GQ chr17_rs11012_GT chr17_rs11012_DP chr17_rs11012_GQ
## 1 99 1 60 99
## 2 99 1 60 99
## 3 99 1 60 99
## 4 99 1 60 99
## 5 99 1 60 99
## 6 99 1 60 99
## chr17_rs393152_GT chr17_rs393152_DP chr17_rs393152_GQ
## 1 1 15 99
## 2 1 15 99
## 3 1 15 99
## 4 1 15 99
## 5 1 15 99
## 6 1 15 99
## chr17_rs12185268_GT chr17_rs12185268_DP chr17_rs12185268_GQ
## 1 1 65 99
## 2 1 65 99
## 3 1 65 99
## 4 1 65 99
## 5 1 65 99
## 6 1 65 99
## chr17_rs199533_GT chr17_rs199533_DP chr17_rs199533_GQ
## 1 0 20 60
## 2 0 20 60
## 3 0 20 60
## 4 0 20 60
## 5 0 20 60
## 6 0 20 60
## chr12_rs34637584_AD_1 chr12_rs34637584_AD_2 chr17_rs11868035_AD_1
## 1 23 0 32
## 2 23 0 32
## 3 23 0 32
## 4 23 0 32
## 5 23 0 32
## 6 23 0 32
## chr17_rs11868035_AD_2 chr17_rs11012_AD_1 chr17_rs11012_AD_2
## 1 21 32 28
## 2 21 32 28
## 3 21 32 28
## 4 21 32 28
## 5 21 32 28
## 6 21 32 28
## chr17_rs393152_AD_1 chr17_rs393152_AD_2 chr17_rs12185268_AD_1
## 1 10 5 27
## 2 10 5 27
## 3 10 5 27
## 4 10 5 27
## 5 10 5 27
## 6 10 5 27
## chr17_rs12185268_AD_2 chr17_rs199533_AD_1 chr17_rs199533_AD_2
## 1 38 20 0
## 2 38 20 0
## 3 38 20 0
## 4 38 20 0
## 5 38 20 0
## 6 38 20 0
## chr12_rs34637584_PL_1 chr12_rs34637584_PL_2 chr12_rs34637584_PL_3
## 1 0 69 726
## 2 0 69 726
## 3 0 69 726
## 4 0 69 726
## 5 0 69 726
## 6 0 69 726
## chr17_rs11868035_PL_1 chr17_rs11868035_PL_2 chr17_rs11868035_PL_3
## 1 498 0 848
## 2 498 0 848
## 3 498 0 848
## 4 498 0 848
## 5 498 0 848
## 6 498 0 848
## chr17_rs11012_PL_1 chr17_rs11012_PL_2 chr17_rs11012_PL_3
## 1 679 0 735
## 2 679 0 735
## 3 679 0 735
## 4 679 0 735
## 5 679 0 735
## 6 679 0 735
## chr17_rs393152_PL_1 chr17_rs393152_PL_2 chr17_rs393152_PL_3
## 1 140 0 242
## 2 140 0 242
## 3 140 0 242
## 4 140 0 242
## 5 140 0 242
## 6 140 0 242
## chr17_rs12185268_PL_1 chr17_rs12185268_PL_2 chr17_rs12185268_PL_3
## 1 1013 0 616
## 2 1013 0 616
## 3 1013 0 616
## 4 1013 0 616
## 5 1013 0 616
## 6 1013 0 616
## chr17_rs199533_PL_1 chr17_rs199533_PL_2 chr17_rs199533_PL_3 UPDRS_part_I
## 1 0 60 709 0
## 2 0 60 709 NA
## 3 0 60 709 1
## 4 0 60 709 0
## 5 0 60 709 1
## 6 0 60 709 4
## UPDRS_part_II UPDRS_part_III UPDRS_part_IV time_visit PD
## 1 2 12 NA 0 0
## 2 NA NA NA 54 0
## 3 4 29 NA 18 0
## 4 2 39 NA 24 0
## 5 11 42 0 42 0
## 6 3 20 NA 12 0
## COGSTATE COGDECLN FNCDTCOG COGDXCL EDUCYRS RAWHITE
## 1 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## 2 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## 3 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## 4 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## 5 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## 6 Normal Cognition (PD-NC) No No 90% - 100% 16 Yes
## APPRDX
## 1 Parkinson's disease
## 2 Parkinson's disease
## 3 Parkinson's disease
## 4 Parkinson's disease
## 5 Parkinson's disease
## 6 Parkinson's diseaseLongtitudinal analysis with different time points of UPDRS measurements
# use complete dataset with the result
DataMissOm<-na.omit(subset(ppmi_long,select=c(time_visit,PD,FID_IID,COGSTATE,COGDECLN,FNCDTCOG,COGDXCL,EDUCYRS,L_superior_parietal_gyrus_ComputeArea
,L_superior_parietal_gyrus_Volume,R_superior_parietal_gyrus_ComputeArea,R_superior_parietal_gyrus_Volume,L_putamen_ComputeArea,L_putamen_Volume,R_putamen_Volume
,R_putamen_ShapeIndex,L_caudate_ComputeArea,L_caudate_Volume,R_caudate_ComputeArea,R_caudate_Volume,chr12_rs34637584_GT
,chr17_rs11868035_GT,chr17_rs11012_GT,chr17_rs393152_GT,chr17_rs12185268_GT,chr17_rs199533_GT,Sex,Weight,Age,UPDRS_part_I,UPDRS_part_II,UPDRS_part_III)))
#normalize continuous variables
for(i in c(8:20,28:32)){
DataMissOm[,i]<-scale(DataMissOm[,i])
}
library(geepack)
DataMissOm$COGSTATE<-as.numeric(DataMissOm$COGSTATE)
DataMissOm$COGDECLN<-as.numeric(DataMissOm$COGDECLN)
DataMissOm$FNCDTCOG<-as.numeric(DataMissOm$FNCDTCOG)
DataMissOm$COGDXCL<-as.numeric(DataMissOm$COGDXCL)
model_gee_UPDRS<-geeglm(PD~L_superior_parietal_gyrus_ComputeArea
+L_superior_parietal_gyrus_Volume+R_superior_parietal_gyrus_ComputeArea+R_superior_parietal_gyrus_Volume+L_putamen_ComputeArea+L_putamen_Volume+R_putamen_Volume
+R_putamen_ShapeIndex+L_caudate_ComputeArea+L_caudate_Volume+R_caudate_ComputeArea+R_caudate_Volume+chr12_rs34637584_GT
+chr17_rs11868035_GT+chr17_rs11012_GT+chr17_rs393152_GT+chr17_rs12185268_GT+chr17_rs199533_GT+Sex+Weight+Age
+UPDRS_part_I+UPDRS_part_II+UPDRS_part_III+FID_IID+COGSTATE+COGDECLN+FNCDTCOG+COGDXCL+EDUCYRS,
data=DataMissOm,waves=time_visit,family="binomial",id=FID_IID,corstr="exchangeable",scale.fix=TRUE)
summary(model_gee_UPDRS)##
## Call:
## geeglm(formula = PD ~ L_superior_parietal_gyrus_ComputeArea +
## L_superior_parietal_gyrus_Volume + R_superior_parietal_gyrus_ComputeArea +
## R_superior_parietal_gyrus_Volume + L_putamen_ComputeArea +
## L_putamen_Volume + R_putamen_Volume + R_putamen_ShapeIndex +
## L_caudate_ComputeArea + L_caudate_Volume + R_caudate_ComputeArea +
## R_caudate_Volume + chr12_rs34637584_GT + chr17_rs11868035_GT +
## chr17_rs11012_GT + chr17_rs393152_GT + chr17_rs12185268_GT +
## chr17_rs199533_GT + Sex + Weight + Age + UPDRS_part_I + UPDRS_part_II +
## UPDRS_part_III + FID_IID + COGSTATE + COGDECLN + FNCDTCOG +
## COGDXCL + EDUCYRS, family = "binomial", data = DataMissOm,
## id = FID_IID, waves = time_visit, corstr = "exchangeable",
## scale.fix = TRUE)
##
## Coefficients:
## Estimate Std.err Wald
## (Intercept) 1.787e+17 6.714e+14 70831.65
## L_superior_parietal_gyrus_ComputeArea -4.700e+17 2.633e+15 31867.29
## L_superior_parietal_gyrus_Volume 5.226e+17 2.836e+15 33961.30
## R_superior_parietal_gyrus_ComputeArea 3.618e+17 1.968e+15 33785.88
## R_superior_parietal_gyrus_Volume -4.222e+17 2.330e+15 32820.75
## L_putamen_ComputeArea -1.330e+17 9.865e+14 18176.48
## L_putamen_Volume 2.165e+17 1.166e+15 34476.22
## R_putamen_Volume -7.282e+16 4.836e+14 22677.60
## R_putamen_ShapeIndex -3.817e+15 1.923e+14 393.72
## L_caudate_ComputeArea -2.169e+17 1.567e+15 19144.96
## L_caudate_Volume 1.112e+17 1.159e+15 9199.90
## R_caudate_ComputeArea 9.570e+16 1.045e+15 8379.14
## R_caudate_Volume 1.932e+16 7.656e+14 636.44
## chr12_rs34637584_GT -1.266e+17 1.230e+15 10595.16
## chr17_rs11868035_GT 2.584e+16 3.206e+14 6497.19
## chr17_rs11012_GT 2.435e+15 6.828e+14 12.71
## chr17_rs393152_GT 1.212e+16 7.579e+14 255.77
## chr17_rs12185268_GT -1.241e+17 1.290e+15 9252.73
## chr17_rs199533_GT 7.307e+16 8.266e+14 7814.16
## Sex 5.194e+16 6.038e+14 7398.75
## Weight -1.305e+16 2.352e+14 3075.64
## Age -1.208e+16 2.022e+14 3568.28
## UPDRS_part_I 1.019e+16 1.301e+14 6133.81
## UPDRS_part_II 2.386e+15 2.648e+14 81.22
## UPDRS_part_III 5.018e+16 3.686e+14 18537.46
## FID_IID -3.435e+13 3.649e+11 8860.45
## COGSTATE 5.995e+15 2.795e+14 460.15
## COGDECLN -2.453e+16 3.557e+14 4753.03
## FNCDTCOG -1.571e+17 7.565e+14 43103.79
## COGDXCL 1.578e+16 6.795e+14 539.41
## EDUCYRS 3.086e+16 2.071e+14 22193.59
## Pr(>|W|)
## (Intercept) < 2e-16 ***
## L_superior_parietal_gyrus_ComputeArea < 2e-16 ***
## L_superior_parietal_gyrus_Volume < 2e-16 ***
## R_superior_parietal_gyrus_ComputeArea < 2e-16 ***
## R_superior_parietal_gyrus_Volume < 2e-16 ***
## L_putamen_ComputeArea < 2e-16 ***
## L_putamen_Volume < 2e-16 ***
## R_putamen_Volume < 2e-16 ***
## R_putamen_ShapeIndex < 2e-16 ***
## L_caudate_ComputeArea < 2e-16 ***
## L_caudate_Volume < 2e-16 ***
## R_caudate_ComputeArea < 2e-16 ***
## R_caudate_Volume < 2e-16 ***
## chr12_rs34637584_GT < 2e-16 ***
## chr17_rs11868035_GT < 2e-16 ***
## chr17_rs11012_GT 0.000363 ***
## chr17_rs393152_GT < 2e-16 ***
## chr17_rs12185268_GT < 2e-16 ***
## chr17_rs199533_GT < 2e-16 ***
## Sex < 2e-16 ***
## Weight < 2e-16 ***
## Age < 2e-16 ***
## UPDRS_part_I < 2e-16 ***
## UPDRS_part_II < 2e-16 ***
## UPDRS_part_III < 2e-16 ***
## FID_IID < 2e-16 ***
## COGSTATE < 2e-16 ***
## COGDECLN < 2e-16 ***
## FNCDTCOG < 2e-16 ***
## COGDXCL < 2e-16 ***
## EDUCYRS < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Scale is fixed.
##
## Correlation: Structure = exchangeable Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 2.217e+15 1.086e+30
## Number of clusters: 406 Maximum cluster size: 12library(lme4)## Loading required package: Matrixmodel_lmm_UPDRS<-glmer(PD~L_superior_parietal_gyrus_ComputeArea
+L_superior_parietal_gyrus_Volume+R_superior_parietal_gyrus_ComputeArea+R_superior_parietal_gyrus_Volume+L_putamen_ComputeArea+L_putamen_Volume+R_putamen_Volume
+R_putamen_ShapeIndex+L_caudate_ComputeArea+L_caudate_Volume+R_caudate_ComputeArea+R_caudate_Volume+chr12_rs34637584_GT
+chr17_rs11868035_GT+chr17_rs11012_GT+chr17_rs393152_GT+chr17_rs12185268_GT+chr17_rs199533_GT+Sex+Weight+Age+UPDRS_part_I+UPDRS_part_II+UPDRS_part_III+FID_IID+COGSTATE+COGDECLN+FNCDTCOG+COGDXCL+EDUCYRS
+(1|FID_IID),control = glmerControl(optCtrl=list(maxfun=20000)),
data=DataMissOm,family="binomial")
summary(model_lmm_UPDRS)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## PD ~ L_superior_parietal_gyrus_ComputeArea + L_superior_parietal_gyrus_Volume +
## R_superior_parietal_gyrus_ComputeArea + R_superior_parietal_gyrus_Volume +
## L_putamen_ComputeArea + L_putamen_Volume + R_putamen_Volume +
## R_putamen_ShapeIndex + L_caudate_ComputeArea + L_caudate_Volume +
## R_caudate_ComputeArea + R_caudate_Volume + chr12_rs34637584_GT +
## chr17_rs11868035_GT + chr17_rs11012_GT + chr17_rs393152_GT +
## chr17_rs12185268_GT + chr17_rs199533_GT + Sex + Weight +
## Age + UPDRS_part_I + UPDRS_part_II + UPDRS_part_III + FID_IID +
## COGSTATE + COGDECLN + FNCDTCOG + COGDXCL + EDUCYRS + (1 |
## FID_IID)
## Data: DataMissOm
## Control: glmerControl(optCtrl = list(maxfun = 20000))
##
## AIC BIC logLik deviance df.resid
## 193.8 380.9 -64.9 129.8 2530
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.682 0.000 0.000 0.000 0.706
##
## Random effects:
## Groups Name Variance Std.Dev.
## FID_IID (Intercept) 36.3 6.03
## Number of obs: 2562, groups: FID_IID, 406
##
## Fixed effects:
## Estimate Std. Error z value
## (Intercept) -7.82e+01 6.52e+04 0.00
## L_superior_parietal_gyrus_ComputeArea -1.94e+00 6.84e+00 -0.28
## L_superior_parietal_gyrus_Volume 2.34e+00 7.22e+00 0.32
## R_superior_parietal_gyrus_ComputeArea -1.36e-01 6.28e+00 -0.02
## R_superior_parietal_gyrus_Volume 1.31e+00 6.66e+00 0.20
## L_putamen_ComputeArea -3.63e+00 5.75e+00 -0.63
## L_putamen_Volume 2.84e+00 5.15e+00 0.55
## R_putamen_Volume -2.95e-01 3.41e+00 -0.09
## R_putamen_ShapeIndex -2.06e-01 1.35e+00 -0.15
## L_caudate_ComputeArea -6.77e+00 7.93e+00 -0.85
## L_caudate_Volume 3.38e+00 7.58e+00 0.45
## R_caudate_ComputeArea 5.30e+00 8.53e+00 0.62
## R_caudate_Volume -2.08e+00 7.78e+00 -0.27
## chr12_rs34637584_GT -4.51e+00 4.94e+02 -0.01
## chr17_rs11868035_GT -4.92e-01 1.65e+00 -0.30
## chr17_rs11012_GT 7.34e-01 3.40e+00 0.22
## chr17_rs393152_GT 2.92e+00 3.43e+00 0.85
## chr17_rs12185268_GT 3.02e+00 5.76e+00 0.52
## chr17_rs199533_GT -2.90e+00 4.92e+00 -0.59
## Sex 1.09e+00 2.73e+00 0.40
## Weight 1.88e+00 1.35e+00 1.40
## Age 1.83e+00 1.33e+00 1.38
## UPDRS_part_I 3.29e-01 1.70e+00 0.19
## UPDRS_part_II -1.28e+01 4.02e+00 -3.19
## UPDRS_part_III -2.09e+01 5.74e+00 -3.65
## FID_IID -1.60e-03 3.31e-03 -0.48
## COGSTATE 9.42e+00 8.01e+00 1.18
## COGDECLN -4.48e+00 4.12e+00 -1.09
## FNCDTCOG 8.71e+00 6.52e+04 0.00
## COGDXCL -4.27e+00 5.06e+00 -0.84
## EDUCYRS 3.68e-02 1.29e+00 0.03
## Pr(>|z|)
## (Intercept) 0.99904
## L_superior_parietal_gyrus_ComputeArea 0.77698
## L_superior_parietal_gyrus_Volume 0.74594
## R_superior_parietal_gyrus_ComputeArea 0.98267
## R_superior_parietal_gyrus_Volume 0.84431
## L_putamen_ComputeArea 0.52797
## L_putamen_Volume 0.58167
## R_putamen_Volume 0.93113
## R_putamen_ShapeIndex 0.87846
## L_caudate_ComputeArea 0.39352
## L_caudate_Volume 0.65557
## R_caudate_ComputeArea 0.53451
## R_caudate_Volume 0.78918
## chr12_rs34637584_GT 0.99272
## chr17_rs11868035_GT 0.76636
## chr17_rs11012_GT 0.82913
## chr17_rs393152_GT 0.39461
## chr17_rs12185268_GT 0.60026
## chr17_rs199533_GT 0.55589
## Sex 0.68982
## Weight 0.16203
## Age 0.16687
## UPDRS_part_I 0.84611
## UPDRS_part_II 0.00140 **
## UPDRS_part_III 0.00026 ***
## FID_IID 0.62892
## COGSTATE 0.23984
## COGDECLN 0.27759
## FNCDTCOG 0.99989
## COGDXCL 0.39832
## EDUCYRS 0.97715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 31 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
## convergence code: 0
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues
## failure to converge in 20000 evaluationslength(unique(DataMissOm$FID_IID)) #406 patients## [1] 406nrow(DataMissOm) #2562 observations## [1] 2562Longtitudinal analysis with different time point of brain imaging measures
ppmi_base<-read.csv("ppmi_ROI_baseline.csv",header=TRUE)
ppmi_base<-ppmi_base[,c(2,24:26,29:33)] #may add 27 28
ppmi_new<-read.csv("PPMI_GSA_clinical.csv",header=TRUE)
ppmi_imaging<-ppmi_new[,1:335]
merged_dataset<-merge(ppmi_imaging,ppmi_base,by="FID_IID",all.x=TRUE)
DataMissOm<-na.omit(subset(merged_dataset,select=c(VisitID,ResearchGroup,FID_IID,COGSTATE,COGDECLN,FNCDTCOG,COGDXCL,EDUCYRS,L_superior_parietal_gyrus_ComputeArea
,L_superior_parietal_gyrus_Volume,R_superior_parietal_gyrus_ComputeArea,R_superior_parietal_gyrus_Volume,L_putamen_ComputeArea,L_putamen_Volume,R_putamen_Volume
,R_putamen_ShapeIndex,L_caudate_ComputeArea,L_caudate_Volume,R_caudate_ComputeArea,R_caudate_Volume,chr12_rs34637584_GT
,chr17_rs11868035_GT,chr17_rs11012_GT,chr17_rs393152_GT,chr17_rs12185268_GT,chr17_rs199533_GT,Sex,Weight,Age,UPDRS_Part_I_Summary_Score_Baseline,UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline,UPDRS_Part_III_Summary_Score_Baseline)))
#normalize continuous variables
for(i in c(9:20,28:32)){
DataMissOm[,i]<-scale(DataMissOm[,i])
}
library(geepack)
DataMissOm$COGSTATE<-as.numeric(DataMissOm$COGSTATE)
DataMissOm$COGDECLN<-as.numeric(DataMissOm$COGDECLN)
DataMissOm$FNCDTCOG<-as.numeric(DataMissOm$FNCDTCOG)
DataMissOm$COGDXCL<-as.numeric(DataMissOm$COGDXCL)
DataMissOm$PD<-ifelse(DataMissOm$ResearchGroup=="Control",0,1)
model_gee_imaging<-geeglm(PD~L_superior_parietal_gyrus_ComputeArea
+L_superior_parietal_gyrus_Volume+R_superior_parietal_gyrus_ComputeArea+R_superior_parietal_gyrus_Volume+L_putamen_ComputeArea+L_putamen_Volume+R_putamen_Volume
+R_putamen_ShapeIndex+L_caudate_ComputeArea+L_caudate_Volume+R_caudate_ComputeArea+R_caudate_Volume+chr12_rs34637584_GT
+chr17_rs11868035_GT+chr17_rs11012_GT+chr17_rs393152_GT+chr17_rs12185268_GT+chr17_rs199533_GT+Sex+Weight+Age
+UPDRS_Part_I_Summary_Score_Baseline+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline+UPDRS_Part_III_Summary_Score_Baseline+FID_IID+COGSTATE+COGDECLN+FNCDTCOG+COGDXCL+EDUCYRS,
data=DataMissOm,waves=VisitID,family="binomial",id=FID_IID,corstr="exchangeable",scale.fix=TRUE)
summary(model_gee_imaging)##
## Call:
## geeglm(formula = PD ~ L_superior_parietal_gyrus_ComputeArea +
## L_superior_parietal_gyrus_Volume + R_superior_parietal_gyrus_ComputeArea +
## R_superior_parietal_gyrus_Volume + L_putamen_ComputeArea +
## L_putamen_Volume + R_putamen_Volume + R_putamen_ShapeIndex +
## L_caudate_ComputeArea + L_caudate_Volume + R_caudate_ComputeArea +
## R_caudate_Volume + chr12_rs34637584_GT + chr17_rs11868035_GT +
## chr17_rs11012_GT + chr17_rs393152_GT + chr17_rs12185268_GT +
## chr17_rs199533_GT + Sex + Weight + Age + UPDRS_Part_I_Summary_Score_Baseline +
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline +
## UPDRS_Part_III_Summary_Score_Baseline + FID_IID + COGSTATE +
## COGDECLN + FNCDTCOG + COGDXCL + EDUCYRS, family = "binomial",
## data = DataMissOm, id = FID_IID, waves = VisitID, corstr = "exchangeable",
## scale.fix = TRUE)
##
## Coefficients:
## Estimate
## (Intercept) 10.421512
## L_superior_parietal_gyrus_ComputeArea 0.751061
## L_superior_parietal_gyrus_Volume -1.251801
## R_superior_parietal_gyrus_ComputeArea 0.689802
## R_superior_parietal_gyrus_Volume -0.356608
## L_putamen_ComputeArea 0.926960
## L_putamen_Volume -0.609840
## R_putamen_Volume -0.077577
## R_putamen_ShapeIndex 0.256049
## L_caudate_ComputeArea 0.139422
## L_caudate_Volume 0.437466
## R_caudate_ComputeArea 0.009620
## R_caudate_Volume -0.681865
## chr12_rs34637584_GT 48.038347
## chr17_rs11868035_GT -0.109276
## chr17_rs11012_GT -0.252672
## chr17_rs393152_GT -0.998842
## chr17_rs12185268_GT 0.760646
## chr17_rs199533_GT -0.040611
## Sex 0.436308
## Weight -0.005619
## Age -0.493743
## UPDRS_Part_I_Summary_Score_Baseline -0.399624
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 0.994330
## UPDRS_Part_III_Summary_Score_Baseline 4.581105
## FID_IID 0.000511
## COGSTATE -2.959439
## COGDECLN -0.127457
## FNCDTCOG -0.758775
## COGDXCL 0.710111
## EDUCYRS -0.042825
## Std.err Wald
## (Intercept) 3.640233 8.20
## L_superior_parietal_gyrus_ComputeArea 0.667863 1.26
## L_superior_parietal_gyrus_Volume 0.709685 3.11
## R_superior_parietal_gyrus_ComputeArea 0.685233 1.01
## R_superior_parietal_gyrus_Volume 0.741701 0.23
## L_putamen_ComputeArea 0.797878 1.35
## L_putamen_Volume 0.778800 0.61
## R_putamen_Volume 0.387239 0.04
## R_putamen_ShapeIndex 0.158337 2.62
## L_caudate_ComputeArea 0.796874 0.03
## L_caudate_Volume 0.768952 0.32
## R_caudate_ComputeArea 0.698348 0.00
## R_caudate_Volume 0.755324 0.81
## chr12_rs34637584_GT NA NA
## chr17_rs11868035_GT 0.260319 0.18
## chr17_rs11012_GT 0.437211 0.33
## chr17_rs393152_GT 0.480575 4.32
## chr17_rs12185268_GT 0.887839 0.73
## chr17_rs199533_GT 0.758373 0.00
## Sex 0.420170 1.08
## Weight 0.182701 0.00
## Age 0.209700 5.54
## UPDRS_Part_I_Summary_Score_Baseline 0.359180 1.24
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 0.997760 0.99
## UPDRS_Part_III_Summary_Score_Baseline 1.528156 8.99
## FID_IID 0.000570 0.80
## COGSTATE 0.842112 12.35
## COGDECLN 0.819498 0.02
## FNCDTCOG 1.175771 0.42
## COGDXCL 0.691705 1.05
## EDUCYRS 0.075770 0.32
## Pr(>|W|)
## (Intercept) 0.00420 **
## L_superior_parietal_gyrus_ComputeArea 0.26077
## L_superior_parietal_gyrus_Volume 0.07775 .
## R_superior_parietal_gyrus_ComputeArea 0.31409
## R_superior_parietal_gyrus_Volume 0.63066
## L_putamen_ComputeArea 0.24532
## L_putamen_Volume 0.43360
## R_putamen_Volume 0.84122
## R_putamen_ShapeIndex 0.10585
## L_caudate_ComputeArea 0.86111
## L_caudate_Volume 0.56942
## R_caudate_ComputeArea 0.98901
## R_caudate_Volume 0.36666
## chr12_rs34637584_GT NA
## chr17_rs11868035_GT 0.67465
## chr17_rs11012_GT 0.56332
## chr17_rs393152_GT 0.03767 *
## chr17_rs12185268_GT 0.39159
## chr17_rs199533_GT 0.95729
## Sex 0.29908
## Weight 0.97547
## Age 0.01855 *
## UPDRS_Part_I_Summary_Score_Baseline 0.26588
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 0.31898
## UPDRS_Part_III_Summary_Score_Baseline 0.00272 **
## FID_IID 0.37004
## COGSTATE 0.00044 ***
## COGDECLN 0.87640
## FNCDTCOG 0.51870
## COGDXCL 0.30460
## EDUCYRS 0.57194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Scale is fixed.
##
## Correlation: Structure = exchangeable Link = identity
##
## Estimated Correlation Parameters:
## Estimate Std.err
## alpha 7.05e-12 3.67e-12
## Number of clusters: 423 Maximum cluster size: 3library(lme4)
#model_lmm_imaging<-glmer(PD~L_superior_parietal_gyrus_ComputeArea
# +L_superior_parietal_gyrus_Volume+R_superior_parietal_gyrus_ComputeArea+R_superior_parietal_gyrus_Volume+L_putamen_ComputeArea+L_putamen_Volume+R_putamen_Volume
# +R_putamen_ShapeIndex+L_caudate_ComputeArea+L_caudate_Volume+R_caudate_ComputeArea+R_caudate_Volume+chr12_rs34637584_GT
# +chr17_rs11868035_GT+chr17_rs11012_GT+chr17_rs393152_GT+chr17_rs12185268_GT+chr17_rs199533_GT+Sex+Weight+Age+UPDRS_Part_I_Summary_Score_Baseline+UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline+UPDRS_Part_III_Summary_Score_Baseline+FID_IID+COGSTATE+COGDECLN+FNCDTCOG+COGDXCL+EDUCYRS
# +(1|FID_IID),#control = glmerControl(optCtrl=list(maxfun=20000)),
# data=DataMissOm,family="binomial")
#summary(model_lmm_imaging)
length(unique(DataMissOm$FID_IID))## [1] 423nrow(DataMissOm) ## [1] 643baseline analysis
ppmi_1<-ppmi[ppmi$VisitID==1,]
ppmi_1_complete<-ppmi_1[,c(1,
3:282, # imaging index
283:284,287, #demographic index
288,291,294,297,300,303, #genotype
336,348,360,384,392, #UPDRS+asessment non motor may delete 372
400:404, #clinical index
405:406)]
ppmi_1_complete$ind<-is.na(ppmi_1_complete$chr17_rs199533_GT)
ppmi_1_complete<-ppmi_1_complete[ppmi_1_complete$ind==FALSE,-303]
ppmi_MI<-ppmi_1_complete[,c(291:300)]
# Multiple Imputation
library(mi)## Loading required package: stats4## mi (Version 1.0, packaged: 2015-04-16 14:03:10 UTC; goodrich)## mi Copyright (C) 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 Trustees of Columbia University## This program comes with ABSOLUTELY NO WARRANTY.## This is free software, and you are welcome to redistribute it## under the General Public License version 2 or later.## Execute RShowDoc('COPYING') for details.set.seed(1414)
mdf<-missing_data.frame(ppmi_MI)## NOTE: The following pairs of variables appear to have the same missingness pattern.
## Please verify whether they are in fact logically distinct variables.
## [,1] [,2]
## [1,] "COGDECLN" "FNCDTCOG"
## [2,] "COGDECLN" "COGDXCL"
## [3,] "FNCDTCOG" "COGDXCL"summary(mdf)## UPDRS_Part_I_Summary_Score_Baseline
## Min. : 0.00
## 1st Qu.: 0.00
## Median : 1.00
## Mean : 1.06
## 3rd Qu.: 2.00
## Max. :10.00
## NA's :2
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline
## Min. : 0.00
## 1st Qu.: 1.00
## Median : 3.00
## Mean : 4.41
## 3rd Qu.: 7.00
## Max. :25.00
## NA's :3
## UPDRS_Part_III_Summary_Score_Baseline
## Min. : 0
## 1st Qu.: 3
## Median :16
## Mean :15
## 3rd Qu.:23
## Max. :47
## NA's :2
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline
## Min. : 0.00
## 1st Qu.: 3.00
## Median : 6.00
## Mean : 6.02
## 3rd Qu.: 8.00
## Max. :19.00
## NA's :4
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline
## Min. : 1.00
## 1st Qu.: 5.00
## Median : 5.00
## Mean : 5.23
## 3rd Qu.: 6.00
## Max. :15.00
## NA's :1
## COGSTATE COGDECLN FNCDTCOG
## 1930 : 0 No :374 No :399
## 1941 : 0 Right: 0 Yes : 9
## 1955 : 0 Yes : 34 NA's: 15
## Dementia (PDD) : 5 NA's : 15
## Mild Cognitive Impairment (PD-MCI): 52
## Normal Cognition (PD-NC) :350
## NA's : 16
## COGDXCL EDUCYRS
## 10% - 49% : 5 Min. : 5.0
## 50% - 89% : 57 1st Qu.:14.0
## 90% - 100%:346 Median :16.0
## NA's : 15 Mean :16.1
## 3rd Qu.:18.0
## Max. :26.0
## imputation<-mi(mdf)
complete<-complete(imputation)
c4<-complete[[4]] #extract complete dataset from it from imputed datasets.
# in this case, we randomly chose 4th.
c4<-c4[,1:10]
ppmi_1<-cbind(ppmi_1_complete[,1:290],c4,ppmi_1_complete[,301:302]) #complete dataset
ppmi_1_ROI<-ppmi_1[,c(1,23,24 ,28,29,33,34,38,39,143,144,148,149,282:302)] #complete dataset of regions of interest
write.csv(ppmi_1,"ppmi_complete_baseline.csv")
write.csv(ppmi_1_ROI,"ppmi_ROI_baseline.csv")
#####explore the distribution of baseline data ####
#categorical
col_n<-c(14,17:22,28:31)
#ppmi_1_ROI$APPRDX<-as.numeric(ppmi_1_ROI$APPRDX)
# frequency table
for (i in col_n){
print(colnames(ppmi_1_ROI[i]))
m<-table(ppmi_1_ROI[,i],ppmi_1_ROI[,33])
print(m)
print(fisher.test(m)$p.value)
}## [1] "Sex"
##
## HC PD
## 1 84 193
## 2 39 107
## [1] 0.499
## [1] "chr12_rs34637584_GT"
##
## HC PD
## 0 123 296
## 1 0 4
## [1] 0.327
## [1] "chr17_rs11868035_GT"
##
## HC PD
## 0 67 142
## 1 40 127
## 2 16 31
## [1] 0.16
## [1] "chr17_rs11012_GT"
##
## HC PD
## 0 84 204
## 1 37 86
## 2 2 10
## [1] 0.716
## [1] "chr17_rs393152_GT"
##
## HC PD
## 0 77 186
## 1 40 99
## 2 6 15
## [1] 1
## [1] "chr17_rs12185268_GT"
##
## HC PD
## 0 80 189
## 1 39 98
## 2 4 13
## [1] 0.888
## [1] "chr17_rs199533_GT"
##
## HC PD
## 0 82 193
## 1 39 96
## 2 2 11
## [1] 0.598
## [1] "COGSTATE"
##
## HC PD
## Dementia (PDD) 0 5
## Mild Cognitive Impairment (PD-MCI) 6 60
## Normal Cognition (PD-NC) 117 235
## [1] 2.54e-05
## [1] "COGDECLN"
##
## HC PD
## No 120 264
## Yes 3 36
## [1] 0.00134
## [1] "FNCDTCOG"
##
## HC PD
## No 123 291
## Yes 0 9
## [1] 0.0643
## [1] "COGDXCL"
##
## HC PD
## 10% - 49% 0 5
## 50% - 89% 9 59
## 90% - 100% 114 236
## [1] 0.00118for (i in col_n){
print(colnames(ppmi_1_ROI[i]))
m<-table(ppmi_1_ROI[,i],ppmi_1_ROI[,34])
print(m)
print(fisher.test(m)$p.value)
}## [1] "Sex"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 1 0 84 170 23
## 2 0 39 93 14
## [1] 0.711
## [1] "chr12_rs34637584_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 123 259 37
## 1 0 0 4 0
## [1] 0.523
## [1] "chr17_rs11868035_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 67 127 15
## 1 0 40 107 20
## 2 0 16 29 2
## [1] 0.189
## [1] "chr17_rs11012_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 84 176 28
## 1 0 37 78 8
## 2 0 2 9 1
## [1] 0.733
## [1] "chr17_rs393152_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 77 160 26
## 1 0 40 89 10
## 2 0 6 14 1
## [1] 0.905
## [1] "chr17_rs12185268_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 80 163 26
## 1 0 39 88 10
## 2 0 4 12 1
## [1] 0.906
## [1] "chr17_rs199533_GT"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 0 0 82 167 26
## 1 0 39 86 10
## 2 0 2 10 1
## [1] 0.779
## [1] "COGSTATE"
##
## psychiatric Healthy Control
## Dementia (PDD) 0 0
## Mild Cognitive Impairment (PD-MCI) 0 6
## Normal Cognition (PD-NC) 0 117
##
## Parkinson's disease SWEDD
## Dementia (PDD) 5 0
## Mild Cognitive Impairment (PD-MCI) 47 13
## Normal Cognition (PD-NC) 211 24
## [1] 1.18e-05
## [1] "COGDECLN"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## No 0 120 233 31
## Yes 0 3 30 6
## [1] 0.00184
## [1] "FNCDTCOG"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## No 0 123 257 34
## Yes 0 0 6 3
## [1] 0.0142
## [1] "COGDXCL"
##
## psychiatric Healthy Control Parkinson's disease SWEDD
## 10% - 49% 0 0 5 0
## 50% - 89% 0 9 50 9
## 90% - 100% 0 114 208 28
## [1] 0.00413#continuous
#normality test
col_n<-c(2:13,15,16,23:27,32)
aa<-matrix(NA,20,1)
k<-1
for(i in col_n){
aa[k,1]<-shapiro.test(ppmi_1_ROI[,i])$p.value
k<-k+1
}
k<-1
#for(i in col_n){
# x<-ppmi_1_ROI[ppmi_1_ROI$RECRUITMENT_CAT=="HC",i]
# y<-ppmi_1_ROI[ppmi_1_ROI$RECRUITMENT_CAT=="PD",i]
# aa[k,2]<-t.test(x,y)$p.value
# k<-k+1
#}
aa<-as.data.frame(aa)
rownames(aa)<-colnames(ppmi_1_ROI)[col_n]
colnames(aa)<-c("normality-test (>0.1 normal)")
write.csv(aa,"continuous_descriptive.csv")model-based analysis
#logistic regression
for(i in c(2:13,15,16,23:27,32)){
ppmi_1_ROI[,i]<-scale(ppmi_1_ROI[,i])
}
ppmi_1_ROI<-as.data.frame(ppmi_1_ROI)
levels(ppmi_1_ROI$RECRUITMENT_CAT)<-c(0,1)
ppmi_1_ROI$RECRUITMENT_CAT<-as.numeric(ppmi_1_ROI$RECRUITMENT_CAT)
ppmi_1_ROI$RECRUITMENT_CAT<-ppmi_1_ROI$RECRUITMENT_CAT-1
logistic_ROI<-glm(RECRUITMENT_CAT~.,data=ppmi_1_ROI[,-c(1,34)],maxit=50,family="binomial")
library(MASS)
stepp<-stepAIC(logistic_ROI,trace=FALSE) #stepwise logistic regression
summary(logistic_ROI)##
## Call:
## glm(formula = RECRUITMENT_CAT ~ ., family = "binomial", data = ppmi_1_ROI[,
## -c(1, 34)], maxit = 50)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -6.95e-06 -2.10e-08 2.10e-08 2.10e-08 2.01e-05
##
## Coefficients:
## Estimate
## (Intercept) -2.60e+02
## L_caudate_ComputeArea 4.40e+01
## L_caudate_Volume 3.36e+01
## R_caudate_ComputeArea 1.38e+01
## R_caudate_Volume -7.29e+01
## L_putamen_ComputeArea 1.06e+02
## L_putamen_Volume -9.39e+01
## R_putamen_ComputeArea 5.60e+01
## R_putamen_Volume -5.98e+01
## L_superior_parietal_gyrus_ComputeArea 1.49e+02
## L_superior_parietal_gyrus_Volume -1.96e+02
## R_superior_parietal_gyrus_ComputeArea -3.50e+01
## R_superior_parietal_gyrus_Volume 7.10e+01
## Sex 2.96e+01
## Weight 5.44e+00
## Age -5.12e+01
## chr12_rs34637584_GT -4.31e+02
## chr17_rs11868035_GT -4.24e+01
## chr17_rs11012_GT 6.88e+00
## chr17_rs393152_GT -4.84e+01
## chr17_rs12185268_GT -1.43e+01
## chr17_rs199533_GT -3.41e+01
## UPDRS_Part_I_Summary_Score_Baseline -2.50e+01
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 1.99e+02
## UPDRS_Part_III_Summary_Score_Baseline 3.71e+02
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline -1.05e+01
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline -1.79e+01
## COGSTATEMild Cognitive Impairment (PD-MCI) 3.16e+02
## COGSTATENormal Cognition (PD-NC) 2.70e+02
## COGDECLNYes 7.25e+01
## FNCDTCOGYes -3.18e+02
## COGDXCL50% - 89% 2.59e+02
## COGDXCL90% - 100% 4.09e+02
## EDUCYRS 1.43e+01
## Std. Error
## (Intercept) 1.89e+07
## L_caudate_ComputeArea 1.39e+07
## L_caudate_Volume 1.33e+07
## R_caudate_ComputeArea 1.47e+07
## R_caudate_Volume 1.62e+07
## L_putamen_ComputeArea 8.55e+06
## L_putamen_Volume 8.04e+06
## R_putamen_ComputeArea 1.07e+07
## R_putamen_Volume 1.10e+07
## L_superior_parietal_gyrus_ComputeArea 1.56e+07
## L_superior_parietal_gyrus_Volume 1.74e+07
## R_superior_parietal_gyrus_ComputeArea 1.34e+07
## R_superior_parietal_gyrus_Volume 1.36e+07
## Sex 6.03e+06
## Weight 2.48e+06
## Age 2.60e+06
## chr12_rs34637584_GT 3.42e+07
## chr17_rs11868035_GT 3.32e+06
## chr17_rs11012_GT 8.23e+06
## chr17_rs393152_GT 5.98e+06
## chr17_rs12185268_GT 8.23e+06
## chr17_rs199533_GT 7.76e+06
## UPDRS_Part_I_Summary_Score_Baseline 2.90e+06
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 4.19e+06
## UPDRS_Part_III_Summary_Score_Baseline 3.77e+06
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline 1.66e+06
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 2.86e+06
## COGSTATEMild Cognitive Impairment (PD-MCI) 2.71e+07
## COGSTATENormal Cognition (PD-NC) 2.75e+07
## COGDECLNYes 1.39e+07
## FNCDTCOGYes 1.73e+07
## COGDXCL50% - 89% 2.49e+07
## COGDXCL90% - 100% 2.46e+07
## EDUCYRS 2.40e+06
## z value
## (Intercept) 0
## L_caudate_ComputeArea 0
## L_caudate_Volume 0
## R_caudate_ComputeArea 0
## R_caudate_Volume 0
## L_putamen_ComputeArea 0
## L_putamen_Volume 0
## R_putamen_ComputeArea 0
## R_putamen_Volume 0
## L_superior_parietal_gyrus_ComputeArea 0
## L_superior_parietal_gyrus_Volume 0
## R_superior_parietal_gyrus_ComputeArea 0
## R_superior_parietal_gyrus_Volume 0
## Sex 0
## Weight 0
## Age 0
## chr12_rs34637584_GT 0
## chr17_rs11868035_GT 0
## chr17_rs11012_GT 0
## chr17_rs393152_GT 0
## chr17_rs12185268_GT 0
## chr17_rs199533_GT 0
## UPDRS_Part_I_Summary_Score_Baseline 0
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 0
## UPDRS_Part_III_Summary_Score_Baseline 0
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline 0
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 0
## COGSTATEMild Cognitive Impairment (PD-MCI) 0
## COGSTATENormal Cognition (PD-NC) 0
## COGDECLNYes 0
## FNCDTCOGYes 0
## COGDXCL50% - 89% 0
## COGDXCL90% - 100% 0
## EDUCYRS 0
## Pr(>|z|)
## (Intercept) 1
## L_caudate_ComputeArea 1
## L_caudate_Volume 1
## R_caudate_ComputeArea 1
## R_caudate_Volume 1
## L_putamen_ComputeArea 1
## L_putamen_Volume 1
## R_putamen_ComputeArea 1
## R_putamen_Volume 1
## L_superior_parietal_gyrus_ComputeArea 1
## L_superior_parietal_gyrus_Volume 1
## R_superior_parietal_gyrus_ComputeArea 1
## R_superior_parietal_gyrus_Volume 1
## Sex 1
## Weight 1
## Age 1
## chr12_rs34637584_GT 1
## chr17_rs11868035_GT 1
## chr17_rs11012_GT 1
## chr17_rs393152_GT 1
## chr17_rs12185268_GT 1
## chr17_rs199533_GT 1
## UPDRS_Part_I_Summary_Score_Baseline 1
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 1
## UPDRS_Part_III_Summary_Score_Baseline 1
## X_Assessment_Non.Motor_Epworth_Sleepiness_Scale_Summary_Score_Baseline 1
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 1
## COGSTATEMild Cognitive Impairment (PD-MCI) 1
## COGSTATENormal Cognition (PD-NC) 1
## COGDECLNYes 1
## FNCDTCOGYes 1
## COGDXCL50% - 89% 1
## COGDXCL90% - 100% 1
## EDUCYRS 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5.1001e+02 on 422 degrees of freedom
## Residual deviance: 5.0390e-10 on 389 degrees of freedom
## AIC: 68
##
## Number of Fisher Scoring iterations: 46summary(stepp)##
## Call:
## glm(formula = RECRUITMENT_CAT ~ L_caudate_ComputeArea + R_caudate_ComputeArea +
## R_caudate_Volume + R_putamen_ComputeArea + L_superior_parietal_gyrus_ComputeArea +
## L_superior_parietal_gyrus_Volume + R_superior_parietal_gyrus_ComputeArea +
## R_superior_parietal_gyrus_Volume + Age + chr17_rs393152_GT +
## UPDRS_Part_I_Summary_Score_Baseline + UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline +
## UPDRS_Part_III_Summary_Score_Baseline + X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline +
## EDUCYRS, family = "binomial", data = ppmi_1_ROI[, -c(1, 34)],
## maxit = 50)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.13e-05 -2.10e-08 2.10e-08 2.10e-08 2.43e-05
##
## Coefficients:
## Estimate
## (Intercept) 4717
## L_caudate_ComputeArea 719
## R_caudate_ComputeArea 2747
## R_caudate_Volume -2974
## R_putamen_ComputeArea 284
## L_superior_parietal_gyrus_ComputeArea 1332
## L_superior_parietal_gyrus_Volume -1632
## R_superior_parietal_gyrus_ComputeArea 1932
## R_superior_parietal_gyrus_Volume -2284
## Age -912
## chr17_rs393152_GT -539
## UPDRS_Part_I_Summary_Score_Baseline -312
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 2269
## UPDRS_Part_III_Summary_Score_Baseline 4878
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline -473
## EDUCYRS 368
## Std. Error
## (Intercept) 1809724
## L_caudate_ComputeArea 290702
## R_caudate_ComputeArea 1152221
## R_caudate_Volume 1224145
## R_putamen_ComputeArea 128396
## L_superior_parietal_gyrus_ComputeArea 691206
## L_superior_parietal_gyrus_Volume 811154
## R_superior_parietal_gyrus_ComputeArea 822670
## R_superior_parietal_gyrus_Volume 989893
## Age 352578
## chr17_rs393152_GT 235286
## UPDRS_Part_I_Summary_Score_Baseline 211657
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 883129
## UPDRS_Part_III_Summary_Score_Baseline 1873873
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 195285
## EDUCYRS 179889
## z value
## (Intercept) 0
## L_caudate_ComputeArea 0
## R_caudate_ComputeArea 0
## R_caudate_Volume 0
## R_putamen_ComputeArea 0
## L_superior_parietal_gyrus_ComputeArea 0
## L_superior_parietal_gyrus_Volume 0
## R_superior_parietal_gyrus_ComputeArea 0
## R_superior_parietal_gyrus_Volume 0
## Age 0
## chr17_rs393152_GT 0
## UPDRS_Part_I_Summary_Score_Baseline 0
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 0
## UPDRS_Part_III_Summary_Score_Baseline 0
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 0
## EDUCYRS 0
## Pr(>|z|)
## (Intercept) 1
## L_caudate_ComputeArea 1
## R_caudate_ComputeArea 1
## R_caudate_Volume 1
## R_putamen_ComputeArea 1
## L_superior_parietal_gyrus_ComputeArea 1
## L_superior_parietal_gyrus_Volume 1
## R_superior_parietal_gyrus_ComputeArea 1
## R_superior_parietal_gyrus_Volume 1
## Age 1
## chr17_rs393152_GT 1
## UPDRS_Part_I_Summary_Score_Baseline 1
## UPDRS_Part_II_Patient_Questionnaire_Summary_Score_Baseline 1
## UPDRS_Part_III_Summary_Score_Baseline 1
## X_Assessment_Non.Motor_Geriatric_Depression_Scale_GDS_Short_Summary_Score_Baseline 1
## EDUCYRS 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5.1001e+02 on 422 degrees of freedom
## Residual deviance: 3.9038e-09 on 407 degrees of freedom
## AIC: 32
##
## Number of Fisher Scoring iterations: 38#logistic_complete<-glm(RECRUITMENT_CAT~.,data=ppmi_1[,-c(1,302)],maxit=50,family="binomial")
#stepp<-stepAIC(logistic_complete,trace=FALSE) #stepwise model selection of ROI data
#summary(stepp)
#ppmi_1 complete
ppmi_1_PD1<-ppmi_1[ppmi_1$APPRDX!="psychiatric",]
ppmi_1_PD1<-ppmi_1[ppmi_1$APPRDX!="SWEDD",]
ppmi_1_PD1<-ppmi_1_PD1[,-c(1,302)]
ppmi_1_PD2<-ppmi_1[ppmi_1$APPRDX!="psychiatric",]
ppmi_1_PD2<-ppmi_1[,-c(1,302)]
ppmi_1_PD1$PD1<-ifelse(ppmi_1_PD1$RECRUITMENT_CAT=="HC",1,0)
ppmi_1_PD1<-ppmi_1_PD1[,-300]
ppmi_1_PD1$PD1<-as.factor(ppmi_1_PD1$PD1)Balance cases
#PD1=PD PD2=PD+SWEDD
set.seed(623)
library(unbalanced)## Loading required package: mlr## Loading required package: BBmisc## Loading required package: ggplot2## Loading required package: ParamHelpers## Loading required package: stringi## Loading required package: foreach## Loading required package: doParallel## Loading required package: iterators## Loading required package: paralleln<-ncol(ppmi_1_PD1)
output<-ppmi_1_PD1$PD1
input<-ppmi_1_PD1[ ,-n]
#balance the dataset
data<-ubBalance(X= input, Y=output,type="ubSMOTE", verbose=TRUE)## Proportion of positives after ubSMOTE : 42.9 % of 861 observationsbalancedData<-cbind(data$X,data$Y)
ppmi_1_PD1_balanced<-balancedData
colnames(ppmi_1_PD1_balanced)<-colnames(ppmi_1_PD1)
ppmi_1_PD2$PD2<-ifelse(ppmi_1_PD2$RECRUITMENT_CAT=="HC",1,0)
ppmi_1_PD2<-ppmi_1_PD2[,-300]
ppmi_1_PD2$PD2<-as.factor(ppmi_1_PD2$PD2)
#balance cases
set.seed(623)
require(unbalanced)
n<-ncol(ppmi_1_PD2)
output<-ppmi_1_PD2$PD2
input<-ppmi_1_PD2[ ,-n]
#balance the dataset
data<-ubBalance(X= input, Y=output,type="ubSMOTE", verbose=TRUE)## Proportion of positives after ubSMOTE : 42.9 % of 861 observationsbalancedData<-cbind(data$X,data$Y)
ppmi_1_PD2_balanced<-balancedData
colnames(ppmi_1_PD2_balanced)<-colnames(ppmi_1_PD2)
#numerize the 4 datasets
for(i in 295:298){
ppmi_1_PD1[,i]<-as.numeric(ppmi_1_PD1[,i])
}
for(i in 295:298){
ppmi_1_PD2[,i]<-as.numeric(ppmi_1_PD2[,i])
}
for(i in 295:298){
ppmi_1_PD1_balanced[,i]<-as.numeric(ppmi_1_PD1_balanced[,i])
}
for(i in 295:298){
ppmi_1_PD2_balanced[,i]<-as.numeric(ppmi_1_PD2_balanced[,i])
}
write.csv(ppmi_1_PD1,"PD1_un.csv")
write.csv(ppmi_1_PD2,"PD2_un.csv")
write.csv(ppmi_1_PD1_balanced,"PD1_b.csv")
write.csv(ppmi_1_PD2_balanced,"PD2_b.csv")### Prediction functions ####
# cv 5 functions
library(crossval)##
## Attaching package: 'crossval'## The following object is masked from 'package:mlr':
##
## crossval#adaboost
library(ada)## Loading required package: rpartlibrary(rpart)
my.ada <- function (train.x, train.y, test.x, test.y, negative, formula){
ada.fit <- ada(train.x, train.y)
predict.y <- predict(ada.fit, test.x)
#count TP,NP..
out <- confusionMatrix(test.y, predict.y, negative = negative)
return (out)
}
# svm
library(e1071)##
## Attaching package: 'e1071'## The following object is masked from 'package:mlr':
##
## imputemy.svm <- function (train.x, train.y, test.x, test.y, negative, formula){
svm.fit <- svm(train.x, train.y)
predict.y <- predict(svm.fit, test.x)
#count TP,NP..
out <- confusionMatrix(test.y, predict.y, negative = negative)
return (out)
}
# naive bayesian
my.nb <- function (train.x, train.y, test.x, test.y, negative, formula){
nb.fit <- naiveBayes(train.x, train.y)
predict.y <- predict(nb.fit, test.x)
#count TP,NP..
out <- confusionMatrix(test.y, predict.y, negative = negative)
return (out)
}
# decision trees
library(rpart)
my.detree <- function (train.x, train.y, test.x, test.y, negative, formula){
train.matrix<-cbind(train.x,train.y)
detree.fit <- rpart(train.y~.,method="class",data=train.matrix)
predict.y <- predict(detree.fit, test.x)
out <- confusionMatrix(test.y, predict.y, negative = negative)
return (out)
}
##k-Nearest Neighbor Classiï¬cation
library("class")
my.knn <- function (train.x, train.y, test.x, test.y, negative, formula){
train.matrix<-cbind(train.x,train.y)
test.matrix<-cbind(test.x,test.y)
predict.y <- knn(train=train.matrix,test=test.matrix,cl=train.y,k=2)
out <- confusionMatrix(test.y, predict.y, negative = negative)
return (out)
}
## Kmeans
my.kmeans <- function (train.x, train.y, test.x, test.y, negative, formula){
train.matrix<-cbind(train.x,train.y)
test.matrix<-cbind(test.x,test.y)
kmeans.fit <- kmeans(train.x,2)
predict.y <- kmeans.fit$cluster-1
out <- confusionMatrix(test.y,predict.y, negative = negative)
return (out)
}function dealt with complete dataset
classfi<-function(a=my.ada){
a1<-matrix(NA,4,4)
a2<-matrix(NA,4,6)
#PD1 unbalanced
X <- ppmi_1_PD1[, 1:299]
Y <- ppmi_1_PD1[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[1,]<-cv.out$stat
a2[1,]<-out
#PD2 unbalanced
X <- ppmi_1_PD2[, 1:299]
Y <- ppmi_1_PD2[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[2,]<-cv.out$stat
a2[2,]<-out
#PD1 balanced
X <- ppmi_1_PD1_balanced[, 1:299]
Y <- ppmi_1_PD1_balanced[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[3,]<-cv.out$stat
a2[3,]<-out
#PD2 balanced
X <- ppmi_1_PD2_balanced[, 1:299]
Y <- ppmi_1_PD2_balanced[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[4,]<-cv.out$stat
a2[4,]<-out
classification<-as.data.frame(cbind(a1,a2))
classification$Group<-c("PD1_un","PD2_un","PD1_b","PD2_b")
write.csv(classification,"classification.csv")
print(classification)
}Classification result
# adaboost
classfi()## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 0.8 23.0 51.8 1.6 0.969 0.935 0.985 0.966 0.970 6.84 PD1_un
## 2 1.2 22.6 58.8 2.0 0.962 0.919 0.980 0.950 0.967 6.32 PD2_un
## 3 0.2 73.4 98.2 0.4 0.997 0.995 0.998 0.997 0.996 11.41 PD1_b
## 4 1.8 72.6 96.6 1.2 0.983 0.984 0.982 0.976 0.988 8.09 PD2_b# detree
classfi(a=my.detree)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 93.4 44.2 11.8 5.0 0.363 0.898 0.11217 0.321 0.702 0.11 PD1_un
## 2 120.0 49.2 0.0 0.0 0.291 1.000 0.00000 0.291 NaN NaN PD2_un
## 3 176.0 145.4 20.8 2.2 0.483 0.985 0.10569 0.452 0.904 2.06 PD1_b
## 4 196.4 146.4 0.4 1.2 0.426 0.992 0.00203 0.427 0.250 -1.39 PD2_b#naive bayes
classfi(a=my.nb)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 11.2 8.6 41.4 16.0 0.648 0.350 0.787 0.434 0.721 0.687 PD1_un
## 2 13.4 11.4 46.6 13.2 0.686 0.463 0.777 0.460 0.779 1.100 PD2_un
## 3 55.2 64.8 43.2 9.0 0.627 0.878 0.439 0.540 0.828 1.729 PD1_b
## 4 55.2 65.4 43.2 8.4 0.631 0.886 0.439 0.542 0.837 1.807 PD2_b#svm
classfi(a=my.svm)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 0.4 6.8 52.2 17.8 0.764 0.2764 0.992 0.944 0.746 3.91 PD1_un
## 2 0.6 2.0 59.4 22.6 0.726 0.0813 0.990 0.769 0.724 2.17 PD2_un
## 3 2.0 69.6 96.4 4.2 0.964 0.9431 0.980 0.972 0.958 6.68 PD1_b
## 4 2.6 68.4 95.8 5.4 0.954 0.9268 0.974 0.963 0.947 6.15 PD2_b# knn
classfi(a=my.knn)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 15.2 7.6 37.4 17.0 0.583 0.309 0.711 0.333 0.688 0.0953 PD1_un
## 2 17.2 7.2 42.8 17.4 0.591 0.293 0.713 0.295 0.711 0.0292 PD2_un
## 3 17.8 55.2 80.6 18.6 0.789 0.748 0.819 0.756 0.812 2.5981 PD1_b
## 4 20.4 56.2 78.0 17.6 0.779 0.762 0.793 0.734 0.816 2.5022 PD2_b# kmeans
classfi(a=my.kmeans)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 Group
## 1 87.6 38.4 124 58.4 0.527 0.397 0.587 0.305 0.681 -0.0685 PD1_un
## 2 114.2 44.6 127 52.6 0.507 0.459 0.527 0.281 0.707 -0.0587 PD2_un
## 3 163.6 126.0 232 167.6 0.519 0.429 0.586 0.435 0.580 0.0623 PD1_b
## 4 165.4 124.2 230 169.4 0.514 0.423 0.581 0.429 0.576 0.0185 PD2_bfunction dealt with dataset without UPDRS (and clinical features)
classfi1<-function(a=my.ada){
a1<-matrix(NA,4,4)
a2<-matrix(NA,4,6)
#PD1 unbalanced
X <- ppmi_1_PD1[, 1:289]
Y <- ppmi_1_PD1[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[1,]<-cv.out$stat
a2[1,]<-out
#PD2 unbalanced
X <- ppmi_1_PD2[, 1:289]
Y <- ppmi_1_PD2[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[2,]<-cv.out$stat
a2[2,]<-out
#PD1 balanced
X <- ppmi_1_PD1_balanced[, 1:289]
Y <- ppmi_1_PD1_balanced[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[3,]<-cv.out$stat
a2[3,]<-out
#PD2 balanced
X <- ppmi_1_PD2_balanced[, 1:289]
Y <- ppmi_1_PD2_balanced[,300]
form <- "train.y~."
neg <- 0
set.seed(714)
cv.out <- crossval(a, X, Y, K = 5, B = 1, negative = neg, formula = form)
out <- diagnosticErrors(cv.out$stat)
a1[4,]<-cv.out$stat
a2[4,]<-out
classification<-cbind(a1,a2)
write.csv(classification,"classification2.csv")
print(classification)
}
#adaboost
classfi1(a=my.ada)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 4.0 4.0 48.6 20.6 0.681 0.1626 0.924 0.500 0.702 0.858
## [2,] 4.2 2.0 55.8 22.6 0.683 0.0813 0.930 0.323 0.712 0.162
## [3,] 2.8 58.0 95.6 15.8 0.892 0.7859 0.972 0.954 0.858 4.831
## [4,] 4.6 55.8 93.8 18.0 0.869 0.7561 0.953 0.924 0.839 4.147#detree
classfi1(a=my.detree)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 100 47.4 5.0 1.8 0.339 0.963 0.0475 0.321 0.735 0.2731
## [2,] 115 48.2 5.0 1.0 0.314 0.980 0.0417 0.295 0.833 0.7399
## [3,] 187 139.8 9.8 7.8 0.434 0.947 0.0498 0.428 0.557 -0.0626
## [4,] 189 139.6 7.4 8.0 0.427 0.946 0.0376 0.424 0.481 -0.3830#naive bayes
classfi1(a=my.nb)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 13.0 7.8 39.6 16.8 0.614 0.317 0.753 0.375 0.702 0.347
## [2,] 16.6 8.8 43.4 15.8 0.617 0.358 0.723 0.346 0.733 0.376
## [3,] 72.4 64.2 26.0 9.6 0.524 0.870 0.264 0.470 0.730 0.876
## [4,] 70.6 64.6 27.8 9.2 0.537 0.875 0.283 0.478 0.751 1.017# svm
classfi1(a=my.svm)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0.2 0.4 52.4 24.2 0.684 0.01626 0.996 0.667 0.684 1.47
## [2,] 0.4 0.2 59.6 24.4 0.707 0.00813 0.993 0.333 0.710 0.20
## [3,] 4.8 53.0 93.6 20.8 0.851 0.71816 0.951 0.917 0.818 3.91
## [4,] 4.2 50.4 94.2 23.4 0.840 0.68293 0.957 0.923 0.801 3.88# knn
classfi1(a=my.knn)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 15.2 7.6 37.4 17.0 0.583 0.309 0.711 0.333 0.688 0.0953
## [2,] 17.2 7.2 42.8 17.4 0.591 0.293 0.713 0.295 0.711 0.0292
## [3,] 17.8 55.2 80.6 18.6 0.789 0.748 0.819 0.756 0.812 2.5981
## [4,] 20.4 56.2 78.0 17.6 0.779 0.762 0.793 0.734 0.816 2.5022# kmeans
classfi1(a=my.kmeans)## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## Number of folds: 5
## Total number of CV fits: 5
##
## Round # 1 of 1
## CV Fit # 1 of 5
## CV Fit # 2 of 5
## CV Fit # 3 of 5
## CV Fit # 4 of 5
## CV Fit # 5 of 5
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 87.6 38.4 124 58.4 0.527 0.397 0.587 0.305 0.681 -0.0685
## [2,] 114.2 44.6 127 52.6 0.507 0.459 0.527 0.281 0.707 -0.0587
## [3,] 163.6 126.0 232 167.6 0.519 0.429 0.586 0.435 0.580 0.0623
## [4,] 165.4 124.2 230 169.4 0.514 0.423 0.581 0.429 0.576 0.0185adaboost scores and results
# Varplot, table S2 and table S3
require(ada)
#unbalanced PD1
set.seed(729)
c1<-ada(PD1~.,data=ppmi_1_PD1)
n_c<-ncol(ppmi_1_PD1)-1
PD1_score<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD1","unBalanced")
write.csv(data_top30,"top30.csv")
#unbalanced PD2
set.seed(729)
c1<-ada(PD2~.,data=ppmi_1_PD2)
n_c<-ncol(ppmi_1_PD2)-1
PD2_score<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD2","unBalanced")
write.csv(data_top30,"top30.csv")
#balanced PD1
set.seed(729)
c1<-ada(PD1~.,data=ppmi_1_PD1_balanced)
n_c<-ncol(ppmi_1_PD1_balanced)-1
PD1_score_b<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD1","Balanced")
write.csv(data_top30,"top30.csv")
#balanced PD2
set.seed(729)
c1<-ada(PD2~.,data=ppmi_1_PD2_balanced)
n_c<-ncol(ppmi_1_PD2_balanced)-1
PD2_score_b<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD2","Balanced")
write.csv(data_top30,"top30.csv")
write.csv(PD1_score,"trial1.csv")
write.csv(PD2_score,"trial2.csv")
write.csv(PD1_score_b,"trial3.csv")
write.csv(PD2_score_b,"trial4.csv")
########without UPDRS
#unbalanced PD1
set.seed(729)
c1<-ada(PD1~.,data=ppmi_1_PD1[,-c(290:294)])
n_c<-ncol(ppmi_1_PD1[,-c(290:294)])-1
PD1_score_noUPDRS<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD1","unBalanced")
write.csv(data_top30,"top30.csv")
#unbalanced PD2
set.seed(729)
c1<-ada(PD2~.,data=ppmi_1_PD2[,-c(290:294)])
n_c<-ncol(ppmi_1_PD2[,-c(290:294)])-1
PD2_score_noUPDRS<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD2","unBalanced")
write.csv(data_top30,"top30.csv")
#balanced PD1
set.seed(729)
c1<-ada(PD1~.,data=ppmi_1_PD1_balanced[,-c(290:294)])
n_c<-ncol(ppmi_1_PD1_balanced[,-c(290:294)])-1
PD1_score_b_noUPDRS<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD1","Balanced")
write.csv(data_top30,"top30.csv")
#balanced PD2
set.seed(729)
c1<-ada(PD2~.,data=ppmi_1_PD2_balanced[,-c(290:294)])
n_c<-ncol(ppmi_1_PD2_balanced[,-c(290:294)])-1
PD2_score_b_noUPDRS<-varplot(c1,type="scores",max.var.show=n_c)
top30<-varplot(c1,type="scores")
data_top30<-cbind(names(top30),"PD2","Balanced")
write.csv(data_top30,"top30.csv")
write.csv(PD1_score_noUPDRS,"trial5.csv")
write.csv(PD2_score_noUPDRS,"trial6.csv")
write.csv(PD1_score_b_noUPDRS,"trial7.csv")
write.csv(PD2_score_b_noUPDRS,"trial8.csv")Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.